tag:blogger.com,1999:blog-70580042497206170712024-03-18T02:48:03.541-07:00Statistics CaféStatistics Cafehttp://www.blogger.com/profile/02491182522761341918noreply@blogger.comBlogger20125tag:blogger.com,1999:blog-7058004249720617071.post-58525927896478183932013-11-19T05:13:00.001-08:002013-11-19T05:32:00.091-08:00Cointegration AnalysisHi all! Sashiburi.. Long time no post :)<br />
<br />
This time I'm gonna explain briefly about cointegration analysis, which is very useful in many research. This method is kind of regression analysis but under nonstationary condition. Remember, when we regress for example 2 variables with each of them nonstationary, then what we will have is spurious regression, a regression that means nothing. Hence Professor Engle-Granger found out that, I heard both of them got Nobel prize for this, when 2 variables nonstationary but the residual of the regression is stationary then it means that those two variables are having long term equilibrium, or we call it cointegrated. For example in the PPP (Purchasing Power Parity) case. We regress 2 variables, exchange rate and inflation. No doubt that these two variables data set are nonstationary. Hence we can still make linear regression between those two and check the residual, is it stationary or not. You can test it by Dickey-Fuller test. If it's stationary then PPP holds, otherwise it is spurious regression. So if the residuals are stationary, what's the model? We then use the error correction model, regressing the stationary term of exchange rates and the stationary term of inflation rates and the error correction term, the lag 1 of residual. That's it! So no more worries of the nonstationarity..lol..:))<br />
<br />Olahttp://www.blogger.com/profile/10621476289425731627noreply@blogger.com12tag:blogger.com,1999:blog-7058004249720617071.post-61980492218746061202012-07-13T04:22:00.001-07:002012-07-13T04:22:30.513-07:00Data Envelopment AnalysisHi all, di cafe kali ini mungkin enak ya kalau kita bahas satu analisis yang bisa digunakan untuk memecahkan permasalahan yang datanya menggunakan pengukuran skala likert. Salah satu analisis yang sekarang lagi populer adalah Data Envelopment Analysis [DEA].<br />
<br />
Beberapa sumber mengatakan bahwa DEA sebenarnya termasuk ke dalam analisis multivariat. Tetapi di buku-buku lainnya, mengatakan bahwa DEA sebenarnya hanya sebuah pengembangan dari linear programming. Well, dua-duanya benar :)<br />
DEA adalah sebuah metoda pengembangan berdasarkan prinsip linear programming, yaitu metoda pemecahan masalah yang dibentuk kedalam sebuah model matematika tujuan, dimana dibatasi oleh beberapa konstrain. Atau bahasa lainnya sebuah permasalahan dengan batasan-batasan masalah yang lain (gimana bingung gak? :D). Pernah tahu mata kuliah aljabar linear atau teknik kuantitatif atau operasional riset? Ya intinya adalah itu semua, yang kemudian dikembangkan atau disesuaikan pada permasalahan yang lebih kompleks, misalnya pada variabel yang memiliki sifat multivariat. Artinya permasalahan tersebut harus diukur oleh beberapa variabel lainnya. Hanya saja, tidak seperti SEM atau analisis mulitvariat yang lainnya, metoda DEA dikhususkan kepada pencarian nilai efisiensi.<br />
<br />
nilai efisiensi = (nilai ouput/input)<br />
<br />
Dimana nilai efisiensi tersebut dibentuk kedalam sebuah model matematis tertentu sesuai dengan permasalhannya. Demikian pula nilai output dan nilai inputnya, merupakan model matematis yang biasanya terbentuk atas DMU (Decesion Making Unit) tertentu. Lalu bagaimana caranya menggunakan DEA? Tenang saja, ada software khusus yang dapat menyelesaikan masalah DEA, bahkan jika variabelnya tidak terlalu banyak perhitungan DEA dapat menggunakan Excel saja. Karena pada prinsipnya adalah linear programming solver. :)<br />
<br />
Masih bingung? Oke, saya coba bahas dengan mereview sebuah jurnal menarik mengenai aplikasi DEA. Judul jurnalnya adalah "Measuring the efficiency of customer satisfaction and loyalty for mobile phone brands with DEA", ditulis oleh Erkan Bayraktar, 2012.<br />
<br />
Bayraktar meneliti tingkat loyalitas dan kepuasan pelanggan terhadap beberapa merek telepon seluler. Langkah penelitiannya adalah sebagai berikut:<br />
1. Membuat model antara input dan ouput permasalahan yang diteliti.<br />
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2. Mendaftarkan faktor/elemen-elemen yang termasuk kedalam setiap bagian faktor input dan output.<br />
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3. Membentuk model matematis dari model awal<br />
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4. Mengambil data, dengan cara menggunakan kuisioner dan pengukuran skala likert dan teknik survei standar.<br />
5. Mengolah data menggunakan Lingo/Excel/DEA Solver atau lainnya<br />
6. Melakukan analisis lanjutan seperti ranking kurskal wallis atau semacamnya (analisis lanjutan ini tidak harus dilakukan, biasanya dilakukan jika peneliti ingin mengetahui lebih lanjut kelayakan ranking dari pengaruh dari setiap faktor dengan menggunakan tes ranking apakah benar terdapat perbedaan ranking pada model).<br />
7. Mengambil kesimpulan<br />
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Conclusion<br />
These findings are also confirmed by the efficiency scores where Motorola and Nokia were ranked as the least efficient brands, respectively, as shown in Table 3. These findings might also be construed as such that the users of both brands tend to have some conflicting perceptions regarding their own mobile phones relative to the other brands. This study has important implications for practice. Without a doubt, the competition for greater market share is intensifying within the mobile phone industry in Turkey. A more focused approach to building up a novel competitive edge is vital for success (or mere survival) in this volatile market.<br />
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<br />
Gimana? Mau coba DEA untuk Tugas Akhir atau penelitian kelembagaan atau untuk riset client? kenapa tidak? ^_^...<br />
<br />
sumber:<br />
Bayraktar, Erkan. 2012. Measuring the efficiency of customer satisfaction and loyalty for mobile phone brands with DEA. Elsevier.<br />
<br />
Cooper, William. 2000. Data Envelopment Analysis. London.<br />
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<br />ilmahttp://www.blogger.com/profile/01994707442013237811noreply@blogger.com13tag:blogger.com,1999:blog-7058004249720617071.post-2255033895022699592012-06-18T19:12:00.000-07:002012-07-09T01:46:31.551-07:00Analytical Hierarchy Process<br />
<div class="MsoNormal" style="line-height: 150%; margin-bottom: 0.0001pt; text-align: justify;">
<b><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;"> Pemilihan Strategi – Proses Analisis Hirarki</span></b></div>
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<br /></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;"> <b>Konsep
AHP</b></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;"> </span><span style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;"></span></div>
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<br /></div>
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<span style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">AHP
merupakan metode pengambilan keputusan dari suatu masalah multifaktor yang
kompleks yang disusun menjadi suatu hirarki. AHP menggunakan proses dan
aplikasi yang berbasis matematik untuk prioritas alternatif. Analisis AHP cocok
digunakan pada permasalahan yang melibatkan kriteria-kriteria kualitatif yang
sulit dikonversi ke dalam bentuk data kuantitatif. Pengukuran dilakukan melalui
perbandingan berpasangan dan berdasarkan pada pendapat dari para ahli dalam
perolehan skala prioritasnya. </span></div>
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<span style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Dalam
memecahkan persoalan dengan analisis logis eksplisit, ada tiga prinsip yang
mendasari pemikiran AHP, yakni: prinsip menyusun hirarki, prinsip menetapkan
prioritas, dan prinsip menentukan konsistensi logis.</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">2.5.2<span style="font: 7pt "Times New Roman";"> <b>
</b></span></span><b><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Penyusunan
Hierarki </span></b></div>
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<span style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">AHP mewakili pemikiran alamiah yang
cenderung mengelompokkan elemen sistem ke level-level yang berbeda dari
masing-masing level berisi elemen yang serupa. Suatu masalah yang kompleks
dapat diuraikan ke dalam kelompok-kelompoknya yang kemudian diatur menjadi suatu
bentuk hirarki sehingga permasalahan akan tampak lebih terstruktur dan
sistematis. </span></div>
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<span style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Hirarki
didefinisikan sebagai suatu representasi dari sebuah permasalahan yang kompleks
dalam suatu struktur multi level. Level pertama dari suatu hirarki adalah tujuan/sasaran
dari sistem yang akan dicari solusinya. Setelah menetapkan tujuan utama sebagai
level teratas akan disusun level hirarki yang berada di bawahnya yaitu kriteria-kriteria
yang cocok untuk mempertimbangkan atau menilai alternatif yang kita berikan dan
menentukan alternatif tersebut. Tiap kriteria mempunyai intensitas yang
berbeda-beda, dimana kriteria-kriteria yang terdapat pada level yang sama
memiliki kepentingan yang hampir sama pula dan tidak memiliki perbedaan yang
terlalu mencolok, jika perbedaan terlalu besar maka harus dibuatkan level yang
baru. Hirarki dilanjutkan dengan subkriteria (jika mungkin diperlukan) dan
seterusnya ke bawah hingga level terakhir dari alternatif.</span><b><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;"></span></b></div>
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<br /></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">2.5.3<span style="font: 7pt "Times New Roman";"> <b>
</b></span></span><b><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Menetapkan
Prioritas</span></b></div>
<div class="MsoListParagraphCxSpMiddle" style="line-height: 150%; margin: 0cm 0cm 0.0001pt; text-align: justify; text-indent: 35.45pt;">
<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Prioritas
dari kriteria-kriteria dapat</span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;"> </span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">dipandang sebagai bobot atau kontribusi </span><span style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">k</span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">riteria</span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;"> </span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">tersebut terhadap tujuan pengambilan keputusan.</span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">
</span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">AHP melakukan analitis
prioritas kriteria dengan</span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;"> </span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">metode perbandingan berpasangan antar dua </span><span style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">k</span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">riteria</span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;"> </span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">hingga semua kriteria yang ada tercakup. Prioritas</span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">
</span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">ini ditentukan berdasarkan
pandangan para pakar</span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;"> </span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">dan pihak-pihak yang berkepentingan terhadap</span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">
</span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">pengambilan keputusan, baik
secara langsung</span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;"> </span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">(diskusi) maupun tidak (kuesioner).</span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">
</span><span style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;"></span></div>
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<span style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">AHP menggunakan <i>pairwise comparison matrix</i> (matriks perbandingan berpasangan) untuk
menghasilkan bobot relatif antar kriteria maupun alternatif. Suatu kriteria
akan dibandingkan dengan kriteria lainnya dalam hal seberapa penting terhadap
pencapaian tujuan di atasnya.</span></div>
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<span style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Nilai-nilai yang disarankan untuk
membuat matriks perbandingan berpasangan adalah sebagai berikut:</span></div>
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<span style="font-family: "Times New Roman","serif"; font-size: 12pt;">Tabel 2.1 </span><span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt;">Skala Banding Berpasangan </span><span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt;">(</span><span style="color: black; font-family: "Times New Roman Italic"; font-size: 10pt; letter-spacing: -0.15pt;">PairwiseComparison
Scale</span><span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt;">)</span></div>
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<b><span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt;">Tingkat Kepentingan</span></b></div>
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<b><span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt;">Definisi</span></b></div>
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<b><span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt;">Penjelasan</span></b></div>
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<span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt;">1</span></div>
</td>
<td style="border-color: -moz-use-text-color windowtext windowtext -moz-use-text-color; border-style: none solid solid none; border-width: medium 1pt 1pt medium; padding: 0cm 5.4pt; width: 5cm;" width="189"><div class="MsoNormal" style="line-height: 11.5pt; margin: 11.5pt 0cm 0.0001pt;">
<span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt;">Kedua kriteria sama pentingnya (<i>equal</i>)</span></div>
</td>
<td style="border-color: -moz-use-text-color windowtext windowtext -moz-use-text-color; border-style: none solid solid none; border-width: medium 1pt 1pt medium; padding: 0cm 5.4pt; width: 219.7pt;" width="293"><div class="MsoNormal" style="line-height: 11.5pt; margin: 11.5pt 0cm 0.0001pt;">
<span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt;">Kedua kriteria memberikan kontribusi yang sama</span></div>
</td>
</tr>
<tr>
<td style="border-color: -moz-use-text-color windowtext windowtext; border-style: none solid solid; border-width: medium 1pt 1pt; padding: 0cm 5.4pt; width: 68.2pt;" width="91"><div align="center" class="MsoNormal" style="line-height: 11.5pt; margin: 11.5pt 0cm 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt;">3</span></div>
</td>
<td style="border-color: -moz-use-text-color windowtext windowtext -moz-use-text-color; border-style: none solid solid none; border-width: medium 1pt 1pt medium; padding: 0cm 5.4pt; width: 5cm;" width="189"><div class="MsoNormal" style="line-height: 11.5pt; margin: 11.5pt 0cm 0.0001pt;">
<span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt;">Kriteria yang satu sedikit lebih penting dibadingkan
kriteria lainnya (<i>moderat</i>)</span></div>
</td>
<td style="border-color: -moz-use-text-color windowtext windowtext -moz-use-text-color; border-style: none solid solid none; border-width: medium 1pt 1pt medium; padding: 0cm 5.4pt; width: 219.7pt;" width="293"><div class="MsoNormal">
<span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt; line-height: 115%;">Pengalaman dan
pertimbangan sedikit menyukai/memihak kriteria satu dibanding yang lain</span></div>
<div class="MsoNormal" style="line-height: 11.5pt; margin: 11.5pt 0cm 0.0001pt;">
<br /></div>
</td>
</tr>
<tr>
<td style="border-color: -moz-use-text-color windowtext windowtext; border-style: none solid solid; border-width: medium 1pt 1pt; padding: 0cm 5.4pt; width: 68.2pt;" width="91"><div align="center" class="MsoNormal" style="line-height: 11.5pt; margin: 11.5pt 0cm 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt;">5</span></div>
</td>
<td style="border-color: -moz-use-text-color windowtext windowtext -moz-use-text-color; border-style: none solid solid none; border-width: medium 1pt 1pt medium; padding: 0cm 5.4pt; width: 5cm;" width="189"><div class="MsoNormal" style="line-height: 11.5pt; margin: 11.5pt 0cm 0.0001pt;">
<span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt;">Kriteria yang satu esensial atau sangat penting
dibanding kriteria lainnya (<i>strong</i>)</span></div>
</td>
<td style="border-color: -moz-use-text-color windowtext windowtext -moz-use-text-color; border-style: none solid solid none; border-width: medium 1pt 1pt medium; padding: 0cm 5.4pt; width: 219.7pt;" width="293"><div class="MsoNormal">
<span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt; line-height: 115%;">Pengalaman dan
penilaian dengan</span> <span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt; line-height: 115%;">dibanding
yang lain kriteria satu</span> <span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt; line-height: 115%;">menyukai
/ memihak </span></div>
</td>
</tr>
<tr>
<td style="border-color: -moz-use-text-color windowtext windowtext; border-style: none solid solid; border-width: medium 1pt 1pt; padding: 0cm 5.4pt; width: 68.2pt;" width="91"><div align="center" class="MsoNormal" style="line-height: 11.5pt; margin: 11.5pt 0cm 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt;">7</span></div>
</td>
<td style="border-color: -moz-use-text-color windowtext windowtext -moz-use-text-color; border-style: none solid solid none; border-width: medium 1pt 1pt medium; padding: 0cm 5.4pt; width: 5cm;" width="189"><div class="MsoNormal" style="line-height: 11.5pt; margin: 11.5pt 0cm 0.0001pt;">
<span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt;">Kriteria yang satu jelas lebih penting dibanding
kriteria lainnya (<i>very strong</i>)</span></div>
</td>
<td style="border-color: -moz-use-text-color windowtext windowtext -moz-use-text-color; border-style: none solid solid none; border-width: medium 1pt 1pt medium; padding: 0cm 5.4pt; width: 219.7pt;" width="293"><div class="MsoNormal">
<span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt; line-height: 115%;">Kriteria yang
satu dengan kuat disukai dan dominasinya praktek</span> <span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt; line-height: 115%;">tampak nyata dalam praktek</span></div>
<div class="MsoNormal" style="line-height: 11.5pt; margin: 11.5pt 0cm 0.0001pt;">
<br /></div>
</td>
</tr>
<tr>
<td style="border-color: -moz-use-text-color windowtext windowtext; border-style: none solid solid; border-width: medium 1pt 1pt; padding: 0cm 5.4pt; width: 68.2pt;" width="91"><div align="center" class="MsoNormal" style="line-height: 11.5pt; margin: 11.5pt 0cm 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt;">9</span></div>
</td>
<td style="border-color: -moz-use-text-color windowtext windowtext -moz-use-text-color; border-style: none solid solid none; border-width: medium 1pt 1pt medium; padding: 0cm 5.4pt; width: 5cm;" width="189"><div class="MsoNormal" style="line-height: 11.5pt; margin: 11.5pt 0cm 0.0001pt;">
<span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt;">Kriteria yang satu mutlak lebih penting dibanding
kriteria lainnya (<i>extreme</i>)</span></div>
</td>
<td style="border-color: -moz-use-text-color windowtext windowtext -moz-use-text-color; border-style: none solid solid none; border-width: medium 1pt 1pt medium; padding: 0cm 5.4pt; width: 219.7pt;" width="293"><div class="MsoNormal">
<span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt; line-height: 115%;">Bukti-bukti yang
memihak kepada kriteria yang satu atas yang lain berada pada mungkin tertinggi
yang </span><span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.1pt; line-height: 115%;">tingkat persetujuan</span></div>
<div class="MsoNormal" style="line-height: 11.5pt; margin: 11.5pt 0cm 0.0001pt;">
<br /></div>
</td>
</tr>
<tr>
<td style="border-color: -moz-use-text-color windowtext windowtext; border-style: none solid solid; border-width: medium 1pt 1pt; padding: 0cm 5.4pt; width: 68.2pt;" width="91"><div align="center" class="MsoNormal" style="line-height: 11.5pt; margin: 11.5pt 0cm 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt;">2,4,6,8</span></div>
</td>
<td style="border-color: -moz-use-text-color windowtext windowtext -moz-use-text-color; border-style: none solid solid none; border-width: medium 1pt 1pt medium; padding: 0cm 5.4pt; width: 5cm;" width="189"><div class="MsoNormal" style="line-height: 11.5pt; margin: 11.5pt 0cm 0.0001pt;">
<span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt;">Nilai-nilai tengah (<i>intermediate</i>) antara dua nilai yang berdekatan </span></div>
</td>
<td style="border-color: -moz-use-text-color windowtext windowtext -moz-use-text-color; border-style: none solid solid none; border-width: medium 1pt 1pt medium; padding: 0cm 5.4pt; width: 219.7pt;" width="293"><div class="MsoNormal">
<span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt; line-height: 115%;">Diperlukan
kompromi antara dua</span> <span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt; line-height: 115%;">pertimbangan
</span></div>
<div class="MsoNormal" style="line-height: 11.5pt; margin: 11.5pt 0cm 0.0001pt;">
<br /></div>
</td>
</tr>
<tr style="height: 33.55pt;">
<td style="border-color: -moz-use-text-color windowtext windowtext; border-style: none solid solid; border-width: medium 1pt 1pt; height: 33.55pt; padding: 0cm 5.4pt; width: 68.2pt;" width="91"><div align="center" class="MsoNormal" style="line-height: 11.5pt; margin: 11.5pt 0cm 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt;">Resiprok</span></div>
</td>
<td colspan="2" style="border-color: -moz-use-text-color windowtext windowtext -moz-use-text-color; border-style: none solid solid none; border-width: medium 1pt 1pt medium; height: 33.55pt; padding: 0cm 5.4pt; width: 361.45pt;" valign="bottom" width="482"><div class="MsoNormal">
<span style="color: black; font-family: "Times New Roman","serif"; font-size: 10pt; letter-spacing: -0.15pt; line-height: 115%;">Apabila telah
diberikan angka kepada kriteria i dibandingkan kriteria j, maka angka yang
diberikan kepada kriteria j dibandingkan kriteria i adalah kebalikan
(resiproknya) </span></div>
<div class="MsoNormal" style="line-height: 11.5pt; margin: 11.5pt 0cm 0.0001pt;">
<br /></div>
</td>
</tr>
</tbody></table>
<div class="MsoListParagraph" style="line-height: 150%; margin-bottom: 0.0001pt; text-align: justify;">
<br /></div>
<div class="MsoNormal" style="line-height: 150%; margin-bottom: 0.0001pt; text-align: justify;">
<b><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Menentukan
Konsistensi Logis</span></b></div>
<div class="MsoListParagraphCxSpFirst" style="line-height: 150%; margin: 0cm 0cm 0.0001pt; text-align: justify; text-indent: 35.45pt;">
<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">AHP
mempertimbangkan konsistensi logis dalam penilaian yang digunakan untuk
menentukan prioritas.</span><span style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;"> Matriks perbandingan berpasangan
disebut konsisten jika dua aturan berikut terpenuhi:</span></div>
<div align="center" class="MsoListParagraphCxSpMiddle" style="line-height: 150%; margin: 0cm 0cm 0.0001pt 144pt; text-align: center; text-indent: -144pt;">
<span style="font-family: "Freestyle Script"; font-size: 22pt; line-height: 150%;">a<sub>ij
</sub>= a<sub>ij </sub>.a<sub>kj </sub></span><sub><span style="font-family: "Times New Roman","serif"; font-size: 22pt; line-height: 150%;">...
(1)</span></sub><sub><span style="font-family: "Freestyle Script"; font-size: 22pt; line-height: 150%;"></span></sub></div>
<div align="center" class="MsoListParagraphCxSpMiddle" style="line-height: 150%; margin-bottom: 0.0001pt; text-align: center; text-indent: -36pt;">
<span style="font-family: "Freestyle Script"; font-size: 22pt; line-height: 150%;"> a<sub>ij </sub>= 1/a<sub>ji
</sub></span><sub><span style="font-family: "Times New Roman","serif"; font-size: 22pt; line-height: 150%;">...
(2)</span></sub></div>
<div class="MsoListParagraphCxSpMiddle" style="line-height: 150%; margin-bottom: 0.0001pt; text-indent: -36pt;">
<span style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;"> dimana
</span><span style="font-family: "Freestyle Script"; font-size: 16pt; line-height: 150%;">i</span><span style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">,</span><span style="font-family: "Freestyle Script"; font-size: 16pt; line-height: 150%;"> j</span><span style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">,
dan </span><span style="font-family: "Freestyle Script"; font-size: 16pt; line-height: 150%;">k</span><span style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">
merupakan alternatif.</span></div>
<div class="MsoListParagraphCxSpMiddle" style="line-height: 150%; margin: 0cm 0cm 0.0001pt; text-align: justify;">
<span style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Pada
matriks yang konsisten dengan sempurna, semua perbandingan </span><span style="font-family: "Freestyle Script"; font-size: 22pt; line-height: 150%;">a<sub>ij
</sub></span><span style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">adalah
berdasarkan persamaan </span><span style="font-family: "Freestyle Script"; font-size: 22pt; line-height: 150%;">a<sub>ij</sub>=p<sub>i </sub>/p<sub>j</sub></span><span style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">,
dimana </span><span style="font-family: "Freestyle Script"; font-size: 22pt; line-height: 150%;">p<sub>i</sub></span><span style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">
adalah prioritas dari alternatif </span><span style="font-family: "Freestyle Script"; font-size: 22pt; line-height: 150%;">i</span><span style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">
. </span></div>
<div class="MsoListParagraphCxSpLast" style="line-height: 150%; margin-bottom: 0.0001pt; text-indent: -36pt;">
<br /></div>
<table border="0" cellpadding="0" cellspacing="0" class="MsoNormalTable" style="border-collapse: collapse; margin-left: auto; margin-right: auto; text-align: left; width: 320px;">
<tbody>
<tr style="height: 30pt;">
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">p<sub>1</sub>/p<sub>1</sub></span></div>
</td>
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">...</span></div>
</td>
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">p<sub>1 </sub>/p<sub>j</sub></span></div>
</td>
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">...</span></div>
</td>
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">p<sub>1 </sub>/p<sub>n</sub></span></div>
</td>
</tr>
<tr style="height: 30pt;">
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">...</span></div>
</td>
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">1</span></div>
</td>
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">...</span></div>
</td>
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">...</span></div>
</td>
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">...</span></div>
</td>
</tr>
<tr style="height: 30pt;">
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">p<sub>i </sub>/p<sub>1</sub></span></div>
</td>
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">...</span></div>
</td>
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">1</span></div>
</td>
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">...</span></div>
</td>
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">p<sub>i </sub>/p<sub>n</sub></span></div>
</td>
</tr>
<tr style="height: 30pt;">
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">...</span></div>
</td>
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">...</span></div>
</td>
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">...</span></div>
</td>
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">1</span></div>
</td>
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">...</span></div>
</td>
</tr>
<tr style="height: 30pt;">
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">p<sub>n </sub>/p<sub>1</sub></span></div>
</td>
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">...</span></div>
</td>
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">p<sub>n </sub>/p<sub>j</sub></span></div>
</td>
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">...</span></div>
</td>
<td nowrap="nowrap" style="height: 30pt; padding: 0cm 5.4pt; width: 48pt;" valign="bottom" width="64"><div align="center" class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: center;">
<span style="color: black; font-family: "Freestyle Script"; font-size: 22pt;">p<sub>n </sub>/p<sub>n</sub></span></div>
</td>
</tr>
</tbody></table>
<div>
</div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;"></span><b><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Manfaat
AHP</span></b></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Metoda AHP
digunakan untuk dapat menimbulkan gagasan dalam melaksanakan suatu tindakan,
dan untuk mengevaluasi keefektifan tindakan tersebut. Selain itu juga untuk
membantu memecahkan suatu kondisi yang kompleks. Manfaat dari metoda ini
adalah:</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">1.<span style="font: 7pt "Times New Roman";">
</span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Suatu
cara praktis untuk menangaini secara kuantitatif bermacam hubungan fungsional
dalam suatu jaringan yang kompleks.</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">2.<span style="font: 7pt "Times New Roman";">
</span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Suatu
alat yang mampu memadukan pernecanaan ke depan (yang diproyeksikan) dan
perencanaan ke belakang (yang diinginkan) dengan cara interaktid yang
mencerminkan pertimbangan dari semua staf manajerial yang berkepentingan.
Keluarannya adalah aturan-aturan yang eksplisit untuk mengalokasikan sumber daya
di antara berbagai tawaran strategi yang sudah ada ataupun baru – atau untuk
mencapai seperangkat sasaran perusahaan dari berbagai alternative skenario
lingkungan.</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">3.<span style="font: 7pt "Times New Roman";">
</span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Suatu
cara baru untuk:</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">-<span style="font: 7pt "Times New Roman";">
</span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Memadukan
data keras dengan pertimbangan subyektif tentang faktor-faktor tanwujud</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">-<span style="font: 7pt "Times New Roman";">
</span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Memasukkan
pertimbagnan bebearpa orang dan memecahkan konflik</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">-<span style="font: 7pt "Times New Roman";">
</span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Melakukan
analisis sensistivitas dan revisi dengan biaya murah</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">-<span style="font: 7pt "Times New Roman";">
</span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Menggunakan
prioritas marjinal maupun prioritas rata-rata untuk membimbing pengalokasian</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">-<span style="font: 7pt "Times New Roman";">
</span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Meningkatkan
kemampuan manajemen untuk melakukan “perimbangan” secara eksplisit</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">4.<span style="font: 7pt "Times New Roman";">
</span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Suatu
teknik yang melengkali berbagai teknik lain (manfaat/biaya), prioritas,
meminimumkan resiko untuk memilih proyek atau aktivitas.</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">5.<span style="font: 7pt "Times New Roman";">
</span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Suatu
pengganti tunggal untuk aneka ragam skema untuk memproyeksikan masa depan dan
melindungi terhadap resiko dan ketidakpastian.</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">6.<span style="font: 7pt "Times New Roman";">
</span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Sarana
untuk memantau dan membimbing prestasi organisasi ke arah seperangkat tujuan
yang dinamis.</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;"><span style="font: 7pt "Times New Roman";"></span></span><b><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Langkah
AHP</span></b></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Pada dasarnya langkah-langkah melakukan pemilihan
strategi dengan menggunakan AHP adalah sebagai berikut:</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">1.<span style="font: 7pt "Times New Roman";">
</span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Definisikan
perosalan secara rinci berikut dengan pemecahan yang diinginkan</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">2.<span style="font: 7pt "Times New Roman";">
</span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Bentuk
model hierarkri dari sudut pandang managerial menyeluruh (dari tingkat puncak
hingga solusi praktis)</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">3.<span style="font: 7pt "Times New Roman";">
</span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Buatlah
matris banding berpasangan dari setiap kriteria dan elemennya</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">4.<span style="font: 7pt "Times New Roman";">
</span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Memeriksa
semua pertimbangan yang ada dalam matriks yang telah dibentuk. Jika terdapat
dua pertimbangan yang sama, hitung saja rata-rata geometriknya</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">5.<span style="font: 7pt "Times New Roman";">
</span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Bentuk
pertanyaan untuk membandingkan pertimbangan-pertimbangan tersebut kemudian cari
datanya</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">6.<span style="font: 7pt "Times New Roman";">
</span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Lakukan
3 langkah sebelumnya pada setiap tingkat hierarkri</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">7.<span style="font: 7pt "Times New Roman";">
</span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Hitung
vector prioritas, dimana vektor tersebut dihitung secara menyeluruh dari atas
hingga bawah, sehingga vektor prioritas paling bawah adalah vektor prioritas
menyeluruh. Jika ada beberapa buah vektor prioritas, hitung saja rata-rata
aritmatiknya</span></div>
<div class="MsoListParagraphCxSpMiddle" style="line-height: 150%; margin-bottom: 0.0001pt; text-align: justify; text-indent: -18pt;">
<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">8.<span style="font: 7pt "Times New Roman";">
</span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Evaluasi
konsistensi untuk seluruh hierarkri</span></div>
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<span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">9.<span style="font: 7pt "Times New Roman";">
</span></span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 150%;">Buat
kesimpulan dari perhitungan tersebut.</span></div>Olahttp://www.blogger.com/profile/10621476289425731627noreply@blogger.com4tag:blogger.com,1999:blog-7058004249720617071.post-12974524064584283152012-02-21T22:22:00.002-08:002012-02-22T17:28:27.863-08:00Menentukan Rentang Skala LikertSebetulnya, skala likert sudah pernah dibahas, tetapi kali ini saya coba bahas dari sisi lain dan mungkin dengan bahasa yang lebih sederhana. Beberapa hari terakhir, saya bergelut dengan skala likert. Semakin membaca beberapa metoda dan jurnal mengenai skala likert, semakin saya bertanya-tanya, bagaimanakah saya menentukan rentang skala likert yang sesuai dengan penelitian yang saya lakukan? Dari beberapa jurnal yang saya baca, kemudian saya dapat simpulkan sebagai berikut.<br />
<br />
Pada dasarnya skala likert hanyalah sebuah alat. Diibaratkan seperti sebuah penggaris. Ketika kita ingin mengukur panjang sebuah meja kecil kita bisa menggunakan penggaris biasa, tetapi ketika kita ingin mengukur panjang jalanan, maka kita harus menggunakan meteran yang lebih panjang.<br />
<br />
Untuk itu, pertama-tama kenali penelitian yang kita lakukan. Apakah penelitian tersebut mengukur preference atau mengukur kinerja? Seberapa detailkan pengukuran yang kita inginkan. Satu hal yang perlu diperhatikan, rentang skala likert harus memiliki nilai yang tepat kebalikan dengan nilai yang lain. Lebih mudahnya perhatikan contoh penelitian berikut:<br />
<br />
1. Setuju vs Tidak Setuju<br />
Ingin diketahui apakah masyarakat setuju akan suatu pernyataan. Contoh peryataannya adalah 'Pelebaran sungai diperlukan untuk mengatasi banjir'. Maka akan muncul kemunginkan pilihan sebagai berikut<br />
<br />
a- sangat tidak setuju agak tidak setuju agak setuju sangat setuju<br />
b- sangat tidak setuju tidak setuju ragu2 setuju sangat setuju<br />
c- sangat tdk setuju tdk setuju agak tdk setuju agak setuju setuju sangat setuju<br />
<br />
Dan seterusnya, kita bisa membuatnya menjadi 4, 5, 6, atau bahkan 7 pilihan. Tetapi mana yang terbaik? Yang terbaik adalah yang memiliki rentang paling banyak. Dengan kondisi setiap arti dari skala tersebut memiliki arti yang saling bertolak belakang dengan skala yang lain. Misalnya setuju vs tidak setuju, sangat tidak setuju vs sangat setuju, dst. Mengapa? Hal ini menghindari pilihan netral (ragu-ragu), kecuali jika anda ingin mengetahui apakah ada pihak netral. Misalkan untuk peryataan seperti berikut 'Pak Abdul layak menjadi Presiden 2014'.<br />
<br />
2. Bagus vs Tidak Bagus<br />
Ingin diketahui penilaian masyarakat tetang sebuah pernyataan atau kondisi. Misalkan mengenai pelayanan imigrasi. Contoh pernyataannya adalah 'Bagaimanakah pelayanan kantor imigrasi menurut anda?' Dengan demikian sudah tentu kita tidak menginginkan pilihan ragu-ragu atau netral atau nilai yang tidak memiliki pembanding, artinya sebaiknya kita menggunakan rentang skala berjumlah genap. Bisa 4, 6, atau 10 pilihan. Namun untuk penilaian semacam ini, yang lazim dikenal di masyarakat adalah nilai 1 sampai 10. Kesimpulannya untuk jenis skala semacam ini, sebaiknya gunakan rentang skala 10.<br />
<br />
tidak bagus - 1 2 3 4 5 6 7 8 9 10 -bagus<br />
<br />
3. Jangan keliru dengan frekuensi<br />
Terkadang kita menggunakan skala frekuensi sebagai skala likert. Seperti pada pertanyaan berikut, 'Berapa sering anda ke perpustakaan?' <br />
<br />
- tidak pernah, Jarang, kadang-kadang, sering, sangat sering<br />
<br />
Jadi intinya, penentuan rentang skala likert adalah tergantung dari penelitian yang kita lakukan. Tidak ada dasar atau harga mati mengenai aturan penentuan rentang tersebut. Dalam praktiknya memang yang paling baik adalah rentang terbesar dan dengan jumlah genap. Dengan rentang yang besar, kita dapat melihat sebaran atau variasi dari jawaban responden. Namun kekurangannya adalah survey yang dilakukan agak sedikit lebih sulit, karena mengharuskan responden berpikir lebih keras dalam memilih pada rentang skala likert yang besar. Tetapi kembali lagi dari penelitian itu sendiri, seberapa pentingkah detail yang diinginkan dan bagaimana hasil yang ingin dicapai dari penelitian tersebut. Semoga tulisan ini membantu yah ;)<br />
<br />
Disarikan dari:<br />
Hall, Shane. 2010. “How to Use the Likert Scale in Statistical Analysis.” <br />
Markusic, Mayflor. 2009. “Simplifying the Likert Scale.” <br />
Trochim, William M.K. 2006. “Likert Scaling.” Research Methods Knowledge Basedilmahttp://www.blogger.com/profile/01994707442013237811noreply@blogger.com6tag:blogger.com,1999:blog-7058004249720617071.post-72459157251459041892012-02-12T19:07:00.000-08:002012-07-09T01:47:34.870-07:00Penghitungan Angka Kemiskinan BPS vs Bank Dunia<div class="item-page">
Masalah kemiskinan merupakan salah satu persoalan mendasar yang menjadi pusat perhatian pemerintah di negara manapun. Salah satu aspek penting untuk mendukung strategi penanggulangan kemiskinan adalah tersedianya data kemiskinan yang akurat dan tepat sasaran. Pengukuran kemiskinan yang dapat dipercaya dapat menjadi instrumen tangguh bagi pengambil kebijakan dalam memfokuskan perhatian pada kondisi hidup orang miskin. Data kemiskinan yang baik dapat digunakan untuk mengevaluasi kebijakan pemerintah terhadap kemiskinan, membandingkan kemiskinan antar waktu dan daerah, serta menentukan target penduduk miskin dengan tujuan untuk memperbaiki posisi mereka. <br />
<div align="justify" style="text-align: justify;">
<br />
<span style="font-weight: bold;">A. Tujuan </span><br />
1. Mengetahui jumlah dan persentase penduduk miskin menurut daerah perkotaan dan pedesaan.<br />2. Mengetahui karakteristik rumahtangga miskin dan tidak miskin menurut daerah perkotaan dan pedesaan.<br />3. Mengetahui distribusi dan ketimpangan pendapatan secara nasional menurut daerah perkotaan dan pedesaan.<br />4. Untuk mengukur kemiskinan, BPS menggunakan konsep kemampuan memenuhi kebutuhan dasar (basic need approach). Dengan pendekatan ini, kemiskinan dipandang sebagai ketidakmampuan dari sisi ekonomi untuk memenuhi kebutuhan dasar makanan dan bukan makanan yang diukur dari sisi pengeluaran. Dengan kata lain, kemiskinan dipandang sebagai ketidakmampuan dari sisi ekonomi untuk memenuhi kebutuhan makanan maupun non makanan yang bersifat mendasar. Penduduk miskin adalah penduduk yang memiliki rata-rata pengeluaran perkapita per bulan di bawah garis kemiskinan.<br />5. Pengukuran kemiskinan dengan menggunakan konsep kemampuan memenuhi kebutuhan dasar (basic need approach) tidak hanya digunakan oleh BPS tetapi juga oleh negara-negara lain, seperti Armenia, Senegal, Pakistan, Bangladesh, Vietnam, Sierra Leone, dan Gambia.<br />
<br />
<div style="text-align: center;">
PENJELASAN DATA KEMISKINAN</div>
<div style="text-align: center;">
Press Release BPS‐RI<br />Jakarta, 27 Januari 2011</div>
<br />Data Statistik Resmi (official statistics) adalah objektivitas universal. Seluruh dunia mengukur kinerja pembangunan dan eksistensi bangsanya melalui penggunaan indikator statistik yang memenuhi standar pengukuran yang disepakati secara internasional. Pekerjaan statistik selalu dikawal oleh Kode Etik Statistik PBB. Di Indonesia akhir‐akhir ini, di sebagian kalangan, cenderung mispersepsi dalam memahami angka statistik. Terkait data statistik kemiskinan misalnya kekeliruan dimaksud melebar ke mana‐mana.<br />Disadari bahwa salah satu aspek penting untuk mendukung Strategi Penanggulangan Kemiskinan adalah tersedianya data kemiskinan yang akurat dan tepat sasaran. Pengukuran kemiskinan yang dapat dipercaya dapat menjadi instrumen tangguh bagi pengambil kebijakan dalam memfokuskan perhatian pada kondisi hidup orang miskin. Data kemiskinan yang baik dapat digunakan untuk mengevaluasi kebijakan pemerintah terhadap kemiskinan, membandingkan kemiskinan antar waktu dan daerah, serta menentukan target penduduk miskin dengan tujuan untuk memperbaiki kualitas hidup mereka.<br />Secara umum kemiskinan didefinisikan sebagai kondisi dimana seseorang atau sekelompok orang tidak mampu memenuhi hak‐hak dasarnya untuk mempertahankan dan mengembangkan<br />kehidupan yang bermartabat. Definisi yang sangat luas ini menunjukkan bahwa kemiskinan merupakan masalah multi dimensional, sehingga tidak mudah untuk mengukur kemiskinan dan<br />perlu kesepakatan pendekatan pengukuran yang dipakai. Untuk mengukur tingkat kemiskinan di Indonesia, BPS menyediakan 2 jenis data yaitu data kemiskinan makro dan mikro.<br />
<span style="font-weight: bold;">Data Kemiskinan Makro </span><br />
Salah satu konsep penghitungan kemiskinan yang diaplikasikan di banyak negara termasuk Indonesia adalah konsep kemampuan memenuhi kebutuhan dasar (basic needs approach).<br />Dengan konsep ini, kemiskinan dipandang sebagai ketidakmampuan dari sisi ekonomi untuk memenuhi kebutuhan dasar makanan dan bukan makanan. Dalam aplikasinya dihitunglah garis kemiskinan absolut. Penduduk yang memiliki rata‐rata pengeluaran/pendapatan per kapita per bulan di bawah garis kemiskinan disebut penduduk miskin.<br />Penghitungan penduduk miskin dengan pendekatan makro didasarkan pada data sampel bukan data sensus, sehingga hasilnya adalah estimasi (perkiraan). Sumber data yang digunakan adalah Survei Sosial Ekonomi Nasional (Susenas), yang pencacahannya dilakukan setiap bulan Maret dengan jumlah sampel 68.000 rumah tangga. BPS menyajikan data kemiskinan makro sejak tahun 1984 sehingga perkembangan jumlah dan persentase penduduk miskin bisa diikuti dari waktu ke waktu.<br />Data kemiskinan makro yang terakhir dihitung BPS adalah posisi Maret 2010 dan dirilis tanggal 1 Juli 2010. Jumlah dan persentase penduduk miskin dihitung per provinsi dengan garis kemiskinan yang berbeda‐beda. Di DKI Jakarta besaran garis kemiskinan mencapai Rp331.169 per kapita per bulan, sementara di Papua Rp259.128. Data di level nasional merupakan penjumlahan penduduk miskin di seluruh provinsi, sehingga jumlah penduduk miskin di Indonesia pada Maret 2010 sebesar 31,02 juta (13,33 persen dari total penduduk) dengan garis kemiskinan sebesar Rp211.726 per kapita per bulan. Pada bulan Maret 2011 BPS akan kembali melakukan pengumpulan data Susenas dan hasil penghitungan penduduk miskin akan dirilis tanggal 1 Juli 2011.<br />Salah satu data kemiskinan yang mengundang polemik panjang adalah data kemiskinan bulan Maret 2006.<br />BPS mengumumkan jumlah penduduk miskin naik dari 35,1 juta (16,0%) pada Februari 2005 menjadi 39,30 juta (17,8%) pada Maret 2006 karena kenaikan harga BBM.<br />
<br /><span style="font-weight: bold;">Data Kemiskinan Mikro</span><br />Data kemiskinan makro hanya menunjukkan jumlah dan persentase penduduk miskin di setiap daerah berdasarkan estimasi. Data ini berguna untuk perencanaan dan evaluasi program kemiskinan dengan target geografis namun tidak dapat menunjukkan siapa dan dimana alamat<br />penduduk miskin (sasaran) sehingga tidak operasional untuk program penyaluran bantuan langsung dan perlindungan sosial seperti bantuan langsung tunai (BLT), raskin, dan Jamkesmas.<br />Untuk penyaluran bantuan langsung yang memerlukan nama dan alamat target dibutuhkan data kemiskinan mikro. Pengumpulan datanya harus dilakukan secara sensus, bukan sampel. Berbeda dengan metode penghitungan kemiskinan makro yang menggunakan konsep kemampuan memenuhi kebutuhan dasar, pengumpulan data kemiskinan mikro didasarkan pada<br />ciri‐ciri rumah tangga miskin supaya pendataan bisa dilakukan secara cepat dan hemat biaya.<br />Upaya pengumpulan data kemiskinan mikro ini telah dilakukan BPS dua kali yaitu pada bulan Oktober 2005 dan September 2008. Data yang diperoleh disebut data Rumah Tangga Sasaran (RTS), yang mencakup bukan hanya rumah tangga (RT) miskin, tetapi juga RT hampir miskin, yaitu RT yang hidup sedikit di atas garis kemiskinan. Jumlah RTS hasil pendataan bulan September 2008 adalah 17,5 juta rumah tangga dengan jumlah anggota rumah tangga sebesar 60,4 juta jiwa. Namun, sebagian besar publik menggunakan angka 70 juta jiwa, dengan mengasumsikan besarnya rata‐rata anggota rumah tangga adalah 4 orang.<br />
<div style="text-align: justify;">
Jadi, sebetulnya tidak ada dua angka kemiskinan. Data 31,02 juta menunjukkan data penduduk miskin (pendekatan makro), sementara data 60,4 juta jiwa menunjukkan data individu penduduk miskin plus hampir miskin (pendekatan mikro). Selisih di antara keduanya menunjukkan besarnya penduduk hampir miskin di Indonesia. Mereka tidak tergolong miskin tetapi sangat rentan terhadap kemiskinan. Perlu kehati‐hatian dalam membandingkan kedua data kemiskinan tersebut karena metode penghitungan dan tujuan penggunaannya memang berbeda.</div>
<div style="font-weight: bold;">
<br /></div>
<div style="font-weight: bold;">
B. Pengukuran Kemiskinan World Bank</div>
World Bank membuat garis kemiskinan absolut US$ 1 dan US$ 2 PPP (purchasing power parity/paritas daya beli) per hari (bukan nilai tukar US$ resmi) dengan tujuan untuk membandingkan angka kemiskinan antar negara/wilayah dan perkembangannya menurut waktu untuk menilai kemajuan yang dicapai dalam memerangi kemiskinan di tingkat global /internasional.<br /> Angka konversi PPP adalah banyaknya rupiah yang dikeluarkan untuk membeli sejumlah kebutuhan barang dan jasa dimana jumlah yang sama tersebut dapat dibeli sebesar US$ 1 di Amerika Serikat. Angka konversi ini dihitung berdasarkan harga dan kuantitas di masing-masing negara yang dikumpulkan dalam suatu survei yang biasanya dilakukan setiap lima tahun.<br /> Chen dan Ravallion (2001) membuat suatu penyesuaian angka kemiskinan dunia dengan menggunakan garis kemiskinan US$ 1 perhari. Berdasarkan penghitungan yang dilakukan, pada tahun 1993 garis kemiskinan US$ 1 PPP per hari adalah ekuivalen dengan Rp. 20.811,- per bulan.<br /> Garis kemiskinan PPP disesuaikan antar waktu dengan angka inflasi relatif, yaitu menggunakan angka indeks harga konsumen. Pada tahun 2006, garis kemiskinan US$ 1 PPP ekuivalen dengan RP.97.218,- per orang per bulan dan garis kemiskinan US$ 2 PPP ekuivalen dengan RP.194.439,- per orang per bulan. Perbandingan garis kemiskinan dan persentase penduduk miskin di Indonesia tahun 2006 menurut BPS dan World Bank adalah sebagai berikut:</div>
<div align="justify" style="text-align: justify;">
<b>Garis Kemiskinan dan Persentase Penduduk Miskin di Indonesia Tahun 2006</b></div>
<table align="justify" border="0" style="height: 145px; width: 505px;"> <tbody>
<tr style="background-color: blue;"> <td align="center"><b><span style="color: white;"> Sumber</span></b></td> <td align="center"><b><span style="color: white;"> Garis Kemiskinan (Per Hari)</span></b></td> <td align="center"><b><span style="color: white;">Garis Kemiskinan (Per Bulan)</span></b></td> <td align="center"><b><span style="color: white;"> Penduduk Miskin (%)</span></b></td> </tr>
<tr style="background-color: #00cccc;"> <td align="center">BPS</td> <td align="center">Rp. 5.066,57,-<br />
≈ US$ 1,55 PPP</td> <td align="center">Rp. 151.997,-</td> <td align="center">17,80</td> </tr>
<tr style="background-color: #ff99ff;"> <td align="center" rowspan="2">World Bank</td> <td align="center">USS 1 PPP<br />
≈ Rp. 3.240,60,-</td> <td align="center">Rp. 97.218,-</td> <td align="center">7,40</td> </tr>
<tr style="background-color: #ff99ff;"> <td align="center">USS 2 PPP<br />
≈ Rp. 6.841,30,-</td> <td align="center">Rp. 194.439,-</td> <td align="center">49,00</td> </tr>
</tbody> </table>
Bank Dunia memprediksi jumlah penduduk Indonesi berpendapatan di bawah US$2 PPP per orang per hari pada tahun 2008 akan turun 4,6 juta orang dari 105,3 juta orang (45,2 persen) menjadi 100,7 juta orang (42,6 persen). Perhitungan itu dilakukan dengan menggunakan jumlah penduduk 232,9 juta orang pada tahun 2007 dan 236,4 juta orang pada tahun 2008. Perkiraan tersebut dibuat dengan memperhitungkan laju inflasi sekitar 6 persen, dampak kenaikan harga minyak dunia saat ini (sekitar US$94 per barel), dan tercapainya pertumbuhan ekonomi Indonesia tahun depan sebesar 6,4 persen. <br />
<div style="font-weight: bold; text-align: justify;">
C. Metodologi dan Perhitungan Kemiskinan BPS</div>
<div style="text-align: justify;">
1. Sumber Data</div>
<div style="text-align: justify;">
Sumber data utama yang dipakai adalah data SUSENAS (Survei Sosial Ekonomi Nasional). Sebagai informasi tambahan, digunakan hasil survei SKPD (Survei Paket Komoditi Kebutuhan Dasar) yang digunakan untuk memperkirakan proporsi dari pengeluaran masing-masing komoditi pokok non makanan.</div>
<div style="text-align: justify;">
2. Metode</div>
<div style="text-align: justify;">
Metode yang digunakan adalah menghitung garis kemiskinan (GK) yang terdiri dari dua komponen, yaitu Garis Kemiskinan Makanan (GKM) dan Garis Kemiskinan Non-Makanan (GKNM), sebagai berikut:</div>
<div style="text-align: justify;">
<b>GK = GKM + GKNM</b></div>
<div style="text-align: justify;">
Penghitungan Garis Kemiskinan dilakukan secara terpisah untuk daerah perkotaan dan pedesaan. Penduduk miskin adalah penduduk yang memiliki rata-rata pengeluaran perkapita per bulan di bawah Garis Kemiskinan.</div>
<div style="text-align: justify;">
Garis kemiskinan makanan (GKM) merupakan nilai pengeluaran kebutuhan minimum makanan yang disetarakan dengan 2.100 kilokalori perkapita per hari. Patokan ini mengacu pada hasil Widyakarya Pangan dan Gizi 1978. Paket komoditi kebutuhan dasar makanan diwakili oleh 52 jenis komoditi (padi-padian, umbi-umbian, ikan, daging, telur dan susu, sayuran, kacang-kacangan, buah-buahan, minyak dan lemak, dll). Ke-52 jenis komoditi ini merupakan komoditi-komoditi yang paling banyak dikonsumsi oleh penduduk miskin. Jumlah pengeluaran untuk 52 komoditi ini sekitar 70 persen dari total pengeluaran orang miskin.</div>
<div style="text-align: justify;">
Garis kemiskinan non-makanan (GKNM) adalah kebutuhabn minimum untuk perumahan, sandang, pendidikan, dan kesehatan. Paket komoditi kebutuhan dasar non-makanan diwakili oleh 51 jenis komoditi di perkotaan dan 47 jenis komoditi di pedesaan.</div>
<div style="text-align: justify;">
3. Teknik Penghitungan Garis Kemiskinan</div>
<div style="text-align: justify;">
Tahap pertama adalah menentukan penduduk referensi, yaitu 20 persen penduduk yang berada di atas Garis Kemiskinan Sementara, yaitu garis kemiskinan periode lalu yang di-inflate dengan inflasi umum (IHK). Dari penduduk referensi ini kemudian dihitung Garis Kemiskinan Makanan (GKM) dan Garis Kemiskinan Non Makanan (GKNM).</div>
<div style="text-align: justify;">
Garis Kemiskinan Makanan adalah jumlah nilai pengeluaran dari 52 komoditi dasar makanan yang riil dikonsumdi penduduk referensi dan kemudian disetarakan dengan nilai energi 2.100 kilokalori perkapita per hari. Penyetaraan nilai pengeluaran kebutuhan minimum makanan dilakukan dengan menghitung harga rata-rata kalori dari ke-52 komoditi tersebut. Selanjutnya GKM tersebut disetarakan dengan 2.100 kilokalori dengan cara mengalikan 2.100 terhadap harga implisit rata-rata kalori.</div>
<div style="text-align: justify;">
Garis Kemiskinan Non-Makanan merupakan penjumlahan nilai kebutuhan minimum dari komoditi-komoditi non-makanan terpilih yang meliputi perumahan, sandang, pendidikan, dan kesehatan. Nilai kebutuhan minimum per komoditi/sub-kelompok non-makanan dihitung dengan menggunakan suatu rasio pengeluaran komoditi /sub-kelompok tersebut terhadap total pengeluaran komoditi/sub-kelompok yang tercatat dalan data Susenas modul konsumsi. Rasio tersebut dihitung dari hasil Survei Paket Komoditi Kebutuhan Dasar 2004 (SPKKD 2004), yang dilakukan untuk mengumpulkan data pengeluaran konsumsi rumahtangga per komoditi non-makanan yang lebih rinci dibandingkan data Susenas modul konsumsi.</div>
<div style="text-align: justify;">
Garis Kemiskinan merupakan penjumlahan dari Garis Kemiskinan Makanan dan Garis Kemiskinan Non-Makanan. Penduduk yang memiliki rata-rata pengeluaran perkapita per bulan di bawah Garis Kemiskinan dikategorikan sebagai penduduk miskin.</div>
<div style="text-align: justify;">
4. Indikator Kemiskinan</div>
<div style="text-align: justify;">
Berdasarkan pendekatan kebutuhan dasar, ada 3 indikator kemiskinan yang digunakan. Pertama, Head Count Index (HCI-P0), yaitu persentase penduduk yang berada di bawah garis kemiskinan (GK).</div>
<div style="text-align: justify;">
Kedua, Indeks Kedalaman Kemiskinan (Poverty Gap Index-P1) yang merupakan rata-rata kesenjangan pengeluaran masing-masing penduduk miskin terhadap garis kemiskinan. Semakin tinggi nilai indeks, semakin jauh rata-rata pengeluaran penduduk dari garis kemiskinan.</div>
<div style="text-align: justify;">
Ketiga, Indeks Keparahan Kemiskinan (Poverty Severity Index- P2) yang memberikan gambaran mengenai penyebaran pengeluaran di antara penduduk miskin. Semakin tinggi nilai indeks, semakin tinggi ketimpangan pengeluaran di antara penduduk miskin.</div>
<div style="text-align: justify;">
5. Ketersediaan Data</div>
<div style="text-align: justify;">
Badan Pusat Statistik (BPS) pertama kali melakukan penghitungan jumlah dan persentase penduduk miskin pada tahun 1984. Pada saat itu, penghitungan jumlah dan persentase penduduk miskin mencakup periode 1976-1981 dengan menggunakan data Survei Sosial Ekonomi Nasional (Susenas) modul konsumsi. Sejak itu, setiap tiga tahun sekali BPS secara rutin mengeluarkan data jumlah dan persentase penduduk miskin yang disajikan menurut daerah perkotaan dan pedesaan. Sejak tahun 2003, BPS secara rutin mengeluarkan data jumlah dan persentase penduduk miskin setiap tahun. Hal tersebut bisa terwujud karena sejak tahun 2003 BPS mengumpulkan data Susenas Panel Modul Konsumsi setiap bulan Februari atau Maret.</div>
<div style="text-align: justify;">
Data kemiskinan yang diproduksi BPS diterbitkan secara tahunan dalam publikasi yang berjudul ” Analisis dan Penghitungan Tingkat Kemiskinan”. </div>
<div style="text-align: justify;">
D. Data Kemiskinan Mikro Untuk Operasional Bantuan Langsung Tunai (BLT)<br /><br /> Data kemiskinan hasil Susenas merupakan data kemiskinan yang bersifat makro (agregasi nasional, provinsi, dan kabupaten/kota). Data ini hanya menunjukkan jumlah dan persentase penduduk miskin di setiap daerah berdasarkan estimasi, tetapi tidak dapat menunjukkan siapa si miskin dan di mana alamat mereka, sehingga tidak operasional di lapangan. Untuk target sasaran keluarga/rumah tangga secara langsung sangat diperlukan data anggota keluarga/rumah tangga miskin dan lokasi tempat tinggal mereka. Upaya penyediaan data kemiskinan mikro ini dilakukan BPS dengan melaksanakan Pendataan Sosial Ekonomi Penduduk 2005 (PSE05) yang pada dasarnya adalah pendataan keluarga/RTS untuk penyaluran dana BLT tahun 2005/2006.</div>
<div style="text-align: justify;">
Hasil PSE05 adalah direktori RTS yang memuat informasi nama kepala rumah tangga dan lokasi tempat tinggalnya. RTS hasil PSE05 ini mencakup (dikelompokkan ke dalam) tiga kategori, yaitu sangat miskin, miskin, dan mendekati miskin. Berbeda dengan metode penghitungan kemiskinan makro yang menggunakan konsep kemampuan memenuhi kebutuhan dasar (basic needs approach), penentuan RTS PSE05 didasarkan pada pendekatan karakteristik rumah tangga/ ciri-ciri rumah tangga miskin.</div>
<div style="text-align: justify;">
Ada 14 variabel/karakteristik rumah tangga yang dipakai untuk menghitung Indeks Skor RTS. Keempat belas variabel tersebut adalah luas lantai perkapita, jenis lantai, jenis dinding, fasilitas tempat buang air besar, sumber air minum, sumber penerangan, bahan bakar, membeli daging/ayam/susu, frekuensi makan, membeli pakaian baru, kemampuan berobat, lapangan usaha kepala rumah tangga, pendidikan kepala rumah tangga, dan aset yang dimiliki rumah tangga.</div>
<div style="text-align: justify;">
Angka kemiskinan makro yang dihitung dari Susenas secara statistik seharusnya terbanding dengan data RTS kategori miskin dan sangat miskin hasil PSE05. Dari perbandingan kedua set data tersebut dengan menggunakan model regresi logistik diperoleh hasil dengan tingkat kesesuaian (concordant) berkisar antara 80–90 persen. Hal ini berarti ada ketidaksesuaian antara data kemiskinan mikro PSE05 dan data kemiskinan makro Susenas yang berkisar antara 10–20 persen. Jumlah rumah tangga miskin dan sangat miskin hasil PSE05 sekitar 12,2 juta. Jika diasumsikan rata-rata anggota rumah tangga sebesar 4 orang dan dikombinasikan dengan angka koreksi 10–20 persen, maka jumlah penduduk miskin dan sangat miskin hasil PSE05 diperkirakan antara 39,04 juta dan 43,92 juta.</div>
<div style="text-align: justify;">
Dibandingkan dengan angka kemiskinan makro di Indonesia pada Maret 2006 sebesar 39,30 juta mengindikasikan adanya konsistensi antara hasil PSE05 dan penghitungan dari Susenas. Akan tetapi perlu kehati-hatian dalam membandingkan kedua data tersebut karena metode penghitungannya didasarkan pada pendekatan yang berbeda. Data PSE05 akan di-update melalui Pendataan Program Perlindungan Sosial (PPLS) 2008 mulai bulan September 2008 dalam rangka penyiapan database RTS untuk memenuhi kebutuhan data berbagai program perlindungan mulai tahun 200</div>
<div style="text-align: justify;">
sumber: http://mamujukab.bps.go.id/index.php/blokberita/159-kemiskinan</div>
</div>Olahttp://www.blogger.com/profile/10621476289425731627noreply@blogger.com3tag:blogger.com,1999:blog-7058004249720617071.post-11518277896290314672012-02-02T12:30:00.000-08:002012-02-20T00:46:11.635-08:00Prinsip mendasar Multivariat<br />
<ul style="margin-top: 0cm;" type="disc">
<li class="MsoNormal"><span lang="EN">Kenapa dalam
mengestimasi harus dilakukan optimasi (MLE)?</span></li>
<ul style="margin-top: 0cm;" type="circle">
<li class="MsoNormal"><span lang="EN">Salah satu jawabannya
adalah minimize error (misal regresi kita mengestimasi persamaan regresi
dengan cara mencari garis regresi yang memiliki eror terkecil artinya
jarak dari data ke estimasinya paling kecil)</span></li>
<li class="MsoNormal"><span lang="EN">Secara math optimasi
ada dua nesesari optimize dan secara kondisi. Pertama syaratnya optimasi
adalah turunan pertamanya = nol. Turunan pertamanya adalah gradient = nol
dan ini adalah syarat nesesarinya, sementara syarat secara kondisinya
adalah ketika turunan berikutnya adalah negatif (untuk memax) dan positif
(untuk memin). Ilustrasinya adalah grafik kurva ke atas atau kebawah (sin
cos, misalnya)</span></li>
<li class="MsoNormal"><span lang="EN">Mirip prinsipnya
dengan least square error (tapi LSE lebih spesifik)</span></li>
</ul>
<li class="MsoNormal"><span lang="EN">Wilks lamda untuk
varians. </span></li>
<li class="MsoNormal"><span lang="EN">Missing data ada dua,
satu yang sistematic ada yang random. Dan kalau diperbolehkan milih, maka
mising data yang paling aman terjadi pada penelitian kita adalah missing
data yang random. Karena kalau kita mising datanya sistematik maka kita
bisa melihat adanya suatu fungsi pada data kita dan itu berarti ada
kesalahan yang terjadi oleh si peneliti sehingga menghasilkan data yang
missing secara sistematik.</span></li>
<li class="MsoNormal"><span lang="EN">Maka kalau data
hilangnya sistematik maka kita harus mengulang penelitian atau survey.
Tapi kalau mising valuenya random maka bisa diestimasi dengan misal
rata-rata data yang ada, atau nilai regresi data yang ada, atau dll. Atau
bisa juga mendelete variable atau pertanyaan yang banyak missingnya.</span></li>
<li class="MsoNormal"><span lang="EN">Outlier. Kalau sampai
ada outlier jangan buru-buru datanya dibuang. Karena semua data sangat
bernilai. Karena outlier itu sama dengan missing value karena terjadi bisa
karena kesalahan kita atau memang sampelnya yang demikian. </span></li>
<li class="MsoNormal"><span lang="EN">Outlier bisa terjadi
karena salah ambil sampel. Misalnya ketika lagi ujian, maka ketika ada
outlier yang nilainya bagus tetapi karena memang mahasiswa itu adalah
expert dibidang itu maka hal tersebut adalah prosedural. </span></li>
</ul>ilmahttp://www.blogger.com/profile/01994707442013237811noreply@blogger.com1tag:blogger.com,1999:blog-7058004249720617071.post-55509188239037066792012-01-01T20:28:00.001-08:002012-01-04T12:32:34.615-08:00Reaction Paper: Statistical thinking and its role for industrial engineers and managers in the 21st centuryArticle entitled ‘Statistical thinking and its role for industrial engineers and managers in the 21st century’ written by Miltiadis Makrymichalos as a managerial audit journal is a very relevant article to the present situation. Nowadays, most of the managers do not use a statistical way of thinking in making decisions. Despite that many managers use six sigma to improve their business processes, they did not think statistically. I think the statistic is not just a tool, but it was also a basic principle about how to think logically and systematically.<br /><br />The managers only use statistics as a tool when they encounter a problem which is related to data. In my opinion, the managers did not use the statistical thinking because of the difficulty in understanding the statistics principles. Furthermore, they have variety in educational backgrounds. Once they have found an issue or a problem, they will solve it according to their past experience and their knowledge. But when they did meet a problem that generates lot of numerical data, they used the statistics method.<br /><br />According to the article, make decisions with statistical thinking means that the managers have to think the problem as a system as it is explained in the article that ‘All work is a system of interconnected processes'. The managers have to be able to see the variety of any situations and outcomes in any system. Therefore they will be able to analyze or even predict the upcoming situations and outcome of a problem.<br /><br />For example, if we use six sigma we can solve the problem systematically. In the real situation, the managers only use six sigma or other tools as a means to repair. But they did not understand the basic principle or the essence of the six sigma method. In a research and development of the manufacturing, most of managers and engineers are focus on clients more than their own organization. Furthermore, the managers are also use the practical data to solve the problem. This way of thinking will led to an ineffective decision. The statistical thinking will first lead the managers to see the symptoms and any variation on a business process problem first. Statistical Thinking and its principles are not difficult and does not actually need to be profound. But it must be known by the managers. Therefore, when they using tools such as QFD, DOE, Six Sigma, and Pareto charts they will be able to predict where the outcome has risen.<br /><br />In conclusion, I feel devastated because of many managers and engineers are ignoring the statistical thinking. But luckily, this paper summarizes the circumstances that existed at the moment. Not just as a warning, this topic can be a wakeup call to many managers that statistics is not just a daunting and meticulous method, but can be a useful way of thinking to the business in any industry.ilmahttp://www.blogger.com/profile/01994707442013237811noreply@blogger.com0tag:blogger.com,1999:blog-7058004249720617071.post-7847548865727150302011-12-14T17:22:00.000-08:002011-12-14T17:40:50.094-08:00Eigen Value (λ)Pada kuliah statistika, kita sering mendengar istilah “EIGEN VALUE”. Apa sebenarnya yang dimaksud dengan eigen value? Eigen value sering diartikan dengan akar ciri. dalam bahasa yang lebih mudah eigen value merupakan suatu nilai yang menunjukkan seberapa besar pengaruh suatu variabel terhadap pembentukan karakteristik sebuah vektor atau matriks. eigen value dinotasikan dengan λ.<br />
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Jika hanya sekedar mengerti bagaimana rumus dan cara penghitungan eigen value, tentu kita tidak akan pernah paham bagaimana interpretasi dari sebuah angka eigen value. Saya akan mengambil contoh satu mangkuk masakan “soto madura”…hmmm…enaknya… Jika ada 10 orang ditanya mengenai rasa soto tersebut, dan diminta menyebutkan bumbu apa kira-kira yang paling terasa dari soto itu, maka semua orang pasti menyebutkan “garam”. karena garam memberikan rasa asin yang tentu saja dimiliki oleh soto tersebut. Tapi mungkin hanya 1 atau dua orang yang menyebutkan “serai” karena serai adalah bumbu khas yang dimiliki soto yang mungkin tidak dimiliki masakan lain. Atau kunyit…<br />
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Nah, sekarang kalau diminta menebak, kira-kira garam memiliki nilai λ yang paling besar atau paling kecil? ya..garam memiliki λ paling kecil. dan yang memiliki λ paling besar adalah yang memberikan karakteristik atau ciri paling kuat pada soto. dan orang akan memberikan jawaban berbeda-beda sesuai dengan indra perasanya masing-masing. sederhana bukan?? sekarang kita tau makna dari sebuah ukuran statistik “eigen value”<br />
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inspirasi ini diberikan oleh dosen saya di ITS, yang membuat saya banyak mengerti filosofi dari sebuah ukuran statistik. thanx to Drs. Kresnayana Yahya, M.Sc..<br />
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sumber : <a href="http://nuvie81.wordpress.com/">http://nuvie81.wordpress.com/</a><br />
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English version:<br />
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<span class="" id="result_box" lang="en"><span class="hps">In</span> <span class="hps">college</span><span class="hps">,</span> <span class="hps">we often</span> <span class="hps">hear the term</span> <span class="hps atn">"</span><span class="">Eigen</span> <span class="hps">VALUE"</span><span class="">.</span> <span class="hps">What exactly</span> <span class="hps">is meant by</span> <span class="hps">eigen</span> <span class="hps">value</span><span class="">?</span> <span class="hps">Eigen</span> <span class="hps">value</span> <span class="hps">is often defined</span> <span class="hps">by</span> <span class="hps">the root</span> <span class="hps">traits</span><span class="">.</span> As a term <span class="hps">eigen</span> <span class="hps">value</span> <span class="hps">is a</span> <span class="hps">value that</span> <span class="hps">indicates</span> <span class="hps">how much influence</span> <span class="hps">on the formation</span> <span class="hps">of a</span> <span class="hps">variable</span> <span class="hps">characteristic</span> <span class="hps">of a</span> <span class="hps">vector or</span> <span class="hps">matrix</span><span class="">.</span> <span class="hps">eigen</span> <span class="hps">value</span> <span class="hps">denoted by</span> <span class="hps">λ</span><span class="">.</span><br />
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<span class="hps">If</span> we just <span class="hps">only</span> <span class="hps">understand</span> <span class="hps">how</span> <span class="hps">the formula</span> <span class="hps">and</span> <span class="hps">calculation of</span> <span class="hps">eigen</span> <span class="hps">values</span><span class="">,</span> <span class="hps">of course we</span> <span class="hps">will never</span> <span class="hps">understand</span> <span class="hps">how the</span> <span class="hps">interpretation</span> <span class="hps">of</span> <span class="hps">a</span> <span class="hps">number</span> <span class="hps">eigen</span> <span class="hps">value</span><span class="">.</span> <span class="hps">I</span><span class="">'ll</span> <span class="hps">take the example of</span> <span class="hps">one</span> <span class="hps">bowl</span> <span class="hps">dish</span> <span class="hps atn">"</span><span class="">soto</span> <span class="hps">madura</span><span class="">" (or chicken soup)</span> <span class="hps">...</span> <span class="hps">hmmm</span> <span class="hps">...</span> <span class="hps">delicious</span> <span class="hps">...</span> <span class="hps">If</span> <span class="hps">there </span><span class="hps">10 people</span> <span class="hps">were asked</span> <span class="hps">about</span> <span class="hps">the</span> <span class="hps">soup</span> <span class="hps">flavors</span><span class="">,</span> <span class="hps">seasonings</span> <span class="hps">and</span> <span class="hps">asked to name</span> <span class="hps">what</span> <span class="hps">about</span> <span class="hps">the most</span> intriquing things from the <span class="hps">soup</span><span class="">, then</span> <span class="hps">everyone would</span> <span class="hps">say</span> <span class="hps atn">"</span><span class="">salt</span><span class="">"</span><span class="">.</span> <span class="hps">because</span> <span class="hps">salt</span> <span class="hps">gives</span> <span class="hps">a salty taste</span> <span class="hps">which of course</span> <span class="hps">is owned</span> <span class="hps">by</span> <span class="hps">the</span> <span class="hps">soup</span><span class="">.</span> <span class="hps">But maybe</span> <span class="hps">only</span> <span class="hps">one</span> <span class="hps">or</span> <span class="hps">two people</span> <span class="hps atn">mention "</span><span class="">lemon grass</span><span class="">" because</span> <span class="hps">lemongrass</span> <span class="hps">is</span> <span class="hps">owned by</span> <span class="hps">a typical</span> <span class="hps">spice</span> <span class="hps">soup</span> <span class="hps">that may not</span> <span class="hps">have</span> <span class="hps">other dishes</span><span class="">.</span> <span class="hps">Or</span> <span class="hps">turmeric</span> <span class="hps">...</span><br />
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<span class="hps">Now, if</span> we have to guess<span class="">, about a</span> <span class="hps">salt</span> <span class="hps">having</span> <span class="hps">the largest</span> <span class="hps">value of</span> <span class="hps">λ</span> <span class="hps">or</span> <span class="hps">the smallest</span><span class="">?</span> <span class="hps">yes</span> <span class="hps">..</span> <span class="hps">salt</span> <span class="hps">has the</span> <span class="hps">smallest</span> <span class="hps">λ</span><span class="">.</span> <span class="hps">and</span> <span class="hps">which</span> <span class="hps">has the</span> <span class="hps">greatest</span> <span class="hps">λ</span> <span class="hps">is the</span> <span class="hps">characteristic</span> <span class="hps">or</span> <span class="hps">trait</span> <span class="hps">that gives</span> <span class="hps">the most</span> <span class="hps">powerful</span> sense <span class="hps">on the</span> <span class="hps">soup</span><span class="">.</span> <span class="hps">and</span> <span class="hps">people will</span> <span class="hps">give</span> <span class="hps">different</span> <span class="hps">answers</span> <span class="hps">according to the</span> <span class="hps">sense of</span> <span class="hps">feeling on</span> <span class="hps">each.</span> isn't simple? <span class="hps">Now</span> <span class="hps">we</span> <span class="hps">know</span> <span class="hps">the meaning</span> <span class="hps">of</span> <span class="hps">a</span> <span class="hps">statistical</span> <span class="hps">measure of</span> <span class="hps atn">"</span><span class="">eigen</span> <span class="hps">value"</span><br />
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<span class="hps">inspiration</span> <span class="hps">was given</span> <span class="hps">by</span> <span class="hps">my</span> <span class="hps">lecturer</span> <span class="hps">at</span> <span class="hps">ITS</span><span class="">,</span> <span class="hps">which</span> <span class="hps">makes</span> <span class="hps">me a lot</span> <span class="hps">to understand</span> <span class="hps">the philosophy</span> <span class="hps">of</span> <span class="hps">a</span> <span class="hps">statistical measure</span><span class="">.</span> <span class="hps">thanx</span> <span class="hps">to</span> <span class="hps">Drs</span><span class="">.</span> <span class="hps">Kresnayana</span> <span class="hps">Yahya</span><span class="">,</span> <span class="hps">M.Sc.</span><span class="">.</span></span><br />
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<span class="" lang="en"><span class="">source : <a href="http://nuvie81.wordpress.com/">http://nuvie81.wordpress.com/</a></span></span>Statistics Cafehttp://www.blogger.com/profile/02491182522761341918noreply@blogger.com1tag:blogger.com,1999:blog-7058004249720617071.post-8619926548265743042011-12-02T21:03:00.000-08:002011-12-02T21:09:06.850-08:00Memperkirakan kesuksesan Film Box Office: Menggunakan Neural Networks<span lang="">"Apa? Membuat film harus menggunakan regresi logistik? Pusing amat?"<br />
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Tenang dulu, bukan berarti membuat film harus ngejelimet dengan angka, tetapi ternyata jika industri dimanapun baik budaya, hiburan bahkan manufaktur, peran metoda statistik bisa menjadi alat bantu yang menyenangkan ^_^.<br />
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Di negara maju, setiap aspek industrinya tidak lepas dari peran riset dan pengembangan. Data dan analisis merupakan alat penting sebagai salah satu feedback selain dari hasil keuangan (laba-rugi). Salah satunya industri hiburan, dalam hal ini adalah industri film. Kepentingan analisis digunakan sebagai mengetahui atau memetakan sebuah industri. Contohnya bisa terlihat pada jurnal yang akan saya bahas kali ini, </span><span lang="EN"><br />
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<span lang="EN">Judul: "Predicting box-office success of motion pictures with neural networks"<br />
Karya: Ramesh Sharda and Dursun Delen<br />
Tahun: 2006<br />
Penerbit: Elsevier.com</span><span style="font-family: AdvPS6F00;"><span lang=""><br />
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<span style="font-family: AdvPS6F00;"><span lang="">Bagian pemetaan indsutri Amerika dan latar belakang mengapa penelitian ini dilakukan sepertinya tidak perlu saya ceritakan panjang lebar, silahkan baca sendiri jurnalnya (mau jurnalnya? hub kami ^_^). Intinya penelitian ini dilakukan untuk bisa memberikan masukkan pada producer film untuk bisa mengambil keputusan terhadap bagaimana mereka bisa membuat film yang akan menghasilkan kesuksesan.<br />
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Okeh, kita masuk kebagian serunya ya ^_^, bagian tools! Dari judulnya jurnal ini menggunakan neural networks, namun sebenarnya neural networks tersebut merupakan pengembangan dari model-model statistik seperti regresi logistik atau diskriminan analysis.<br />
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Apa sih Neural Networks? Neural networks adalah analisis jaringan antara variabel-variabel. Secara sederhana model hubungan antar variabel dalam statistik adalah Regresi. Namun dalam penelitian ini data yang digunakan adalah data diskrit dan qualitative dimana setiap variable saling berhubungan satu sama lain, maka neural network digunakan untuk mengukur variabel yang mempengaruhi kesuksesan sebuah film. <br />
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Pertama-tama yang harus dilakukan adalah membuat hipotesa awal. Dalam hal ini kita membuat model neural networknya. Dengan melakukan penelitian awal dan diskusi terhadap para ahli maka diperoleh model berikut<br />
Dari model tersebut terdapat 7 variabel yang akan mempengaruhi sebuah kesuksesan film. Kesuksesan tersebut dibagi menjadi 9 kelas (output).</span></span><br />
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<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgQchLIFOQ2R4_W4gZl4cMS12g1qsDkyTPw6SAjjTFfK6tAZx9KR7zSACTmtCIYQpzfGCSUfHCxDQ6hFGuyhTIlk20S5hIsaQ0mTfLsqpA724NUKb3EXNG124Oy0jGHDTV3vfnOAkxCrvZx/s1600/stat+art+-+film+-+pic1.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="282" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgQchLIFOQ2R4_W4gZl4cMS12g1qsDkyTPw6SAjjTFfK6tAZx9KR7zSACTmtCIYQpzfGCSUfHCxDQ6hFGuyhTIlk20S5hIsaQ0mTfLsqpA724NUKb3EXNG124Oy0jGHDTV3vfnOAkxCrvZx/s400/stat+art+-+film+-+pic1.png" width="400" /></a></div><br />
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Berikut variabel yang akan diukur:<br />
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<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEii120o5Pg8Tc3SX8Rw2KC8SR6r19Wc9v2DAhW_GGc0BvPrIdy3bHyosKtg2bEy-HC8ufj7MUzLxrRY6PZW2Zb7rRqTWGKrsSI85JdqO01w5xbTrE2qt1JrhIR5MuUTc7YpcQ1uwUI7z-kc/s1600/stat+art+-+film+-+pic2.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="240" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEii120o5Pg8Tc3SX8Rw2KC8SR6r19Wc9v2DAhW_GGc0BvPrIdy3bHyosKtg2bEy-HC8ufj7MUzLxrRY6PZW2Zb7rRqTWGKrsSI85JdqO01w5xbTrE2qt1JrhIR5MuUTc7YpcQ1uwUI7z-kc/s320/stat+art+-+film+-+pic2.png" width="320" /></a></div><br />
Kemudian dilakukan observasi terhadap data-data setiap tahunnya selama 5 tahun untuk mengetahui bagimana nilai persentase dari variabel-variabel tersebut dalam mempengaruhi performance film. Kemudian dikelompokkan film mana masuk kelas mana. Kelompok tersebut dibentuk dalam sebuah matriks. Jika film tersebut diprediksikan akan masuk kelas "tidak laku" dan dari data menyatakan memang film tersebut "tidak laku" maka hal tersebut diistilahkan sebagai tepat (bingo). Jika tidak maka disebut setidaknya hampir mendekati tepat (1-away).<br />
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Dari data matriks selama 5 tahun (dapat dilihat di jurnal), kemudian dengan neural networks dan atau alat statistik yang lainnya diperoleh sebuah prediksi bingo dan 1-away, sebagai berikut:<br />
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<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjUSNHwaY-H9_idsn6pAnMCvlUUk_XeAlVSi7rUIQaeWZjcnYPf1oCxzIl2KHx1s4FotTMtI-Itr3nmx6V4SySV1NMGkw5RHZfoj3817Cl24hDO3oKl4OflF-5HXe35GhAYQ5Bxfr8dsg5m/s1600/stat+art+-+film+-+pic3.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="190" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjUSNHwaY-H9_idsn6pAnMCvlUUk_XeAlVSi7rUIQaeWZjcnYPf1oCxzIl2KHx1s4FotTMtI-Itr3nmx6V4SySV1NMGkw5RHZfoj3817Cl24hDO3oKl4OflF-5HXe35GhAYQ5Bxfr8dsg5m/s400/stat+art+-+film+-+pic3.png" width="400" /></a></div><br />
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Dari hasil di atas terlihat bahwa standar deviasi terhadap data 1-away yang paling kecil adalah dengan menggunakan Neural Networks. Artinya Neural Network merupakan alat yang paling tepat untuk memprediksikan kesuksesan suatu produk dalam bidang yang memiliki banyak faktor qualitative seperti sosial dan hiburan.<br />
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Setelah didapatkan model menggunakan neural networks selanjutnya dilakukan sensitivitas analisis. Gunanya untuk menilai apakah variable-variable tersebut secara mampu mempengaruhi kesuksesan sebuah film. Hasilnya ternyata hal yang paling mempengaruhi kesuksesan film adalah <b>jumlah layar, efek visual teknologi, dan artis terkenal. </b>Dimana model neural network tersebut dapat memprediksi sebesar 75% akan sukses masuk kelas tertentu sesuai perkiraan producer.<br />
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Sekian penjelasan mengenai penggunan analisis pada sebuah industri hiburan yang katanya tabu jika disandingkan dengan science ^_^..<br />
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Gimana seru kan? model tersebut juga bisa digunakan untuk industri musik, televisi dan lainnya. Tapi di Indonesia gimana? Variable apakah yang mempengaruhi kesuksesan film? Nah, itu belum pernah ada yang melakukannya. ^_^<br />
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Semoga tulisan ini mengispirasi para pembaca yang sedang melakukan penelitian skripsi atau thesis. Silahkan ditanyakan pada kami kalau ada yang kurang jelas dan ingin bantuan untuk mengejarkan analisis ini pada kasus Indonesia atau dimanapun, dengan senang hati kami akan membantu ^_^...Statistics Cafehttp://www.blogger.com/profile/02491182522761341918noreply@blogger.com1tag:blogger.com,1999:blog-7058004249720617071.post-4700658184677447932011-11-14T19:49:00.000-08:002011-11-14T20:02:16.734-08:00Sekilas Sampel<div style="text-align: justify;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgldKyb-PSAPOR9PpQyMP18S95OBBBrlRQgyd2MS1z6W3IecW49N3YeMDqWR-aiFp7C63SAOIuTHg6C0b-rXCJ4oqjufEJNmEy0jvH5N0HZ0rcoVTCQLS7OgNpgV3s83oVhsYYVaNtWg2Xj/s1600/Population+vs+Sample.gif" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="297" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgldKyb-PSAPOR9PpQyMP18S95OBBBrlRQgyd2MS1z6W3IecW49N3YeMDqWR-aiFp7C63SAOIuTHg6C0b-rXCJ4oqjufEJNmEy0jvH5N0HZ0rcoVTCQLS7OgNpgV3s83oVhsYYVaNtWg2Xj/s320/Population+vs+Sample.gif" width="320" /></a></div><span style="font-family: "Times New Roman","serif";">Sampel merupakan suatu hal yang wajib dilibatkan ketika kita melakukan suatu riset. Berapa banyak sampel yang harus diambil, berapa persen proporsinya, dan berapa presisi yang harus ditentukan, merupakan pertanyaan-pertanyaan yang selalu muncul dalam pengambilan sampel.<br />
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Secara umum, syarat sampel yang baik adalah yang dapat mewakili sebanyak mungkin karakteristik populasi. Dengan kata lain, sampel tersebut harus valid, artinya mengukur sesuatu yang harusnya diukur. Tentunya jika ingin meneliti tingkat kecerdasan anak sma tingkat jawa barat, kita tidak hanya meneliti yang sma di kota bandung saja, ya kan. <br />
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Nah, ada 2 hal yang wajib diperhatikan untuk mencapai sampel yang valid, yaitu akurasi dan presisi. <br />
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<b>Akurasi </b>dapat diartikan tingkat ketidakadaan bias dalam sampel. Agar sampel dapat memprediksi dengan baik suatu populasi, sampel harus mempunyai selengkap mungkin karakteristik populasi. Dan perlu diketahui bahwa akurasi prediktibilitas dari suatu sampel tidak bisa dijamin dengan banyaknya sampel.<br />
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<b>Presisi. </b>Jika berbicara mengenai presisi, artinya kita sudah berbicara mengenai estimasi. Presisi mengacu pada persoalan sedekat mana statistik kita dengan parameternya. Contoh, hasil survei berdasarkan sampel yang kita punya, rata-rata pendapatan orang indonesia 5juta, sedangkn berdasarkan perhitungan badan pemerintah yg notabene hasil sensus, rata-rata pendapatan orang Indonesia adalah 5,2 juta. Nah ada perbedaan 0,2 juta dalam estimasi kita yang disebut sampling error. Semakin kecil perbedaan tersebut, semakin tinggi tingkat presisi sampel kita. Presisi sendiri berkaitan dengan interval konfidensi (CI), misalnya CI kita 4,75juta - 5,25juta. Karena statistik kita 5juta, maka perbedaan 0,25juta dari nilai estimasi kita disebut sebagai presisi. Dan perlu diketahui bahwa dalam estimasi, ada juga yang disebut confidence level atau tingkat kepercayaan, dengan besaran biasanya 90%, 95%, atau 99%, yang berarti bahwa dengan besaran tingkat kepercayaan tersebut, kita yakin bahwa rata-rata populasi berada pada selang interval konfidensi yang kita buat. Dianggap bahwa semakin lebar selang kepercayaan, semakin jelek estimasi tersebut (poor estimate). Tentunya setiap peneliti menginginkan selang kepercayaan yang sempit, yang berarti balik lagi ke tingkat presisinya yang harus tinggi. Tapi masalahnya adalah parameter populasi tak pernah diketahui, so bagaimana bisa kita menentukan presisi? <br />
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Sebetulnya ukuran sampel bergantung pada derajat keseragaman, presisi yang dikehendaki, rencana analisis data dan fasilitas yang tersedia (Singarimbun dan Effendi, 1982). Bagaimana cara menentukan presisi, banyak hal yang turut mempengaruhi, misalnya masalah waktu, objek penelitiannya apa, dan biasanya yang paling paling mempengaruhi adalah masalah biaya. :p. <br />
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Presisi diukur oleh simpangan baku (standard error). Makin kecil perbedaan di antara simpangan baku yang diperoleh dari sampel dengan simpangan baku dari populasi, makin tinggi pula tingkat presisinya. Walau tidak selamanya, tingkat presisi mungkin bisa meningkat dengan cara menambahkan jumlah sampel, karena kesalahan mungkin bisa berkurang kalau jumlah sampelnya ditambah ( Kerlinger, 1973 ).<br />
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Akurasi dan presisi, gambaran lebih jelasnya bisa dilihat <a href="http://conflict.lshtm.ac.uk/page_49.htm">disini.</a> <br />
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Tentunya jika kita mencoba idealis, dimana kita menginginkan hasil survei dengan interval konfidensi yang sempit, maka kita setting tingkat presisi yang tinggi pula. Yang pastinya dengan tingkat presisi yang tinggi tersebut kita harus siap dengan jumlah sampel yang cukup besar, karena ukuran sampel berbanding terbalik dengan kuadrat presisi.<br />
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L. Naing, T. Winn, B.N. Rusli mengutarakan artikel yang cukup bagus mengenai perhitungan presisi ini. Saya copas aja semuanya disini ya :D.<br />
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<b> </b><i><b>Determining Precision (d)</b><br />
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What is the appropriate precision for prevalence studies? Most of the books or guides show the steps to calculate the sample size but there is no definite recommendation for appropriate d. Investigators generally ends up with the ball-park figures of the study sizes usually based on their limitations such as financial resources, time or availability of subjects. However, we should calculate the sample size with a reasonable or acceptable precision and then allowing for other limitations. In our experience, it is appropriate to have a precision of 5% if the prevalence of the disease is going to be between 10% and 90%.<br />
This precision will give the width of 95% CI as 10% (e.g. 30% to 40%, or 60% to 70%). However, when the prevalence is going to be below 10% or more than 90%, the precision of 5% seems to be inappropriate. For example, if the prevalence is 1% (in a rare disease) the precision of 5% is obviously crude and it may cause<br />
problems. The obvious problem is that 95% CIs of the estimated prevalence will end up with irrelevant negative lower-bound values or larger than 1 upper bound values as seen in the Table 1. Therefore, we recommend d as a half of P if P is below 0.1 (10%) and if P is above 0.9 (90%), d can be {0.5(1-P)}. For example, if P is 0.04, investigators may use d=0.02, and if P is 0.98, we recommend d=0.01. Figure 1 is plotted with this recommendation. Investigators may also select a smaller precision than what we suggest if they wish.However, if there is a resource limitation,investigators may use a larger d. In case of apreliminary study, investigators may use a larger d (e.g. >10%). However, justification for the selection of d should be stated clearly (e.g. limitation of resources) in their research proposal so that reviewers will be well informed. In addition, the larger d should meet the assumption of normal approximation that we will discuss later.</i><br />
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Artikel lengkapnya, unduh<a href="http://www.google.co.id/url?sa=t&rct=j&q=PRACTICAL%2BISSUES%2BIN%2BDETERMINING%2BSAMPLE%2BSIZE%2BPARAMETERS%2BDetermining%2BPrecision%2B%28d%29&source=web&cd=1&ved=0CB0QFjAA&url=http%3A%2F%2Fwww.kck.usm.my%2Fppsg%2Faos%2FVol_1%2F09_14_Ayub.pdf&ei=gePBTvGkEY3KrAep-PTECw&usg=AFQjCNHO7hD9WoJw87DpbySRtproGWo8pA&sig2=ulZz3EYZvlo5VeXop5pldA&cad=rja"> disini nih..</a><br />
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Intinya adalah, dalam menentukan jumlah sampel, batasan yang harus diperhatikan:</span></div><div style="text-align: justify;"><ul><li><span style="font-family: "Times New Roman","serif";">Tentukan tujuan risetnya</span></li>
<li><span style="font-family: "Times New Roman","serif";">Tentukan respondennya, dari sini kita bisa tau pake sampling apa, random atau non random, kalo random, pake metode sampling random apa, kalo non random pake metode sampling non random apa</span></li>
<li><span style="font-family: "Times New Roman","serif";">Bikin kerangka samplingnya</span></li>
<li><span style="font-family: "Times New Roman","serif";">Merunut ke tujuan survei, pastinya kita udah tau responden di wilayah mana yg mau disurvei..inget musti valid..</span></li>
<li><span style="font-family: "Times New Roman","serif";">Merunut ke tujuan survei lagi, pastinya kita udah tau berapa lama waktu yang kita punya untuk riset kita, begitupun dengan dana yang kita punyai. Jika Anda udah menentukan jumlah sampel, dan ternyata dengan waktu atau dana yang Anda punya tidak cukup, maka mungkin sampelnya harus dikurangin, dengan cara menurunkan tingkat presisinya. Jika ternyata sample yang Anda ambil bahkan kurang dari sepertiganya, it's fine, asalkan dapat dipertanggungjawabkan, dan pastinya langkah pengambilan sampel sesuai dengan kaidah statistik plus sampel Anda representatif :D.</span></li>
</ul><div></div><div></div><div></div></div>Statistics Cafehttp://www.blogger.com/profile/02491182522761341918noreply@blogger.com2tag:blogger.com,1999:blog-7058004249720617071.post-4561944279391756252011-10-11T02:17:00.000-07:002011-10-11T02:26:45.705-07:00Introduction of multivariat - Dasar Statistik<div class="MsoNormal" style="font-family: arial; margin-bottom: 0.0001pt;"><span lang="EN">Sebelum masuk ke multivariat yang sebenarnya, kita harus tahu dasar sebelumnya. Maka yang berikut ini adalah intro statistik:</span></div><div class="MsoNormal" style="font-family: arial; margin-bottom: 0.0001pt;"><span lang="EN" style="font-size: 100%;"><br />
</span></div><div class="MsoNormal" style="font-family: arial; margin-bottom: 0.0001pt;"><span lang="EN" style="font-size: 100%;">- Statistic itu sebenarnya berasal dari kata apa ayoo?? (aku yg backgroundnya jurusan statistic seumur-umur belum pernah peduli dengan asal usul kata Statistic. Begitu juga dosen-dosen terdahulu ku, ^_^)</span></div><div class="MsoNormal" style="font-family: arial; margin-bottom: 0.0001pt;"><span lang="EN">Statistik berasal dari kata statista, bahasa italia yang berarti state person. Seseorang di suatu lembaga yang bertugas untuk mengambil keputusan. Orang tersebut menggunakan data dan informasi untuk mendukung keputusannya tersebut.</span></div><div class="MsoNormal" style="font-family: arial; margin-bottom: 0.0001pt;"><span lang="EN" style="font-size: 100%;"><br />
</span></div><div class="MsoNormal" style="font-family: arial; margin-bottom: 0.0001pt;"><span lang="EN">- Lalu mengapa kita harus belajar statistik?</span></div><div class="MsoNormal" style="font-family: arial; margin-bottom: 0.0001pt;"><span lang="EN" style="font-size: 100%;">Agar kita bisa mengambil keputusan yang valid atau setidaknya memiliki dasar yang jelas, yaitu data atau informasi. Data dan informasi yang ada pasti tidak ada yang sama. Semuanya memiliki variabilitas yang heterogen. Karena itu ada statistik.</span></div><div class="MsoNormal" style="font-family: arial; margin-bottom: 0.0001pt;"><span lang="EN"><br />
</span></div><div class="MsoNormal" style="font-family: arial; margin-bottom: 0.0001pt;"><span lang="EN">- Lompat sedikit ke conjoint analysis. Apa sih Conjoint analysis (dosenku yang sekarang adalah ahlinya conjoint. dia pernah menerbitkan buku khusus menengai conjoint. mau? call me ^_^).</span></div><div class="MsoNormal" style="font-family: arial; margin-bottom: 0.0001pt;"><span lang="EN">Conjoint analisis bisa digunakan pada bidang marketing riset gunanya untuk mengetahui preferensi konsumen.</span></div><div class="MsoNormal" style="font-family: arial; margin-bottom: 0.0001pt;"><span lang="EN"> </span></div><div class="MsoNormal" style="font-family: arial; margin-bottom: 0.0001pt;"><span lang="EN" style="font-size: 100%;"><br />
</span></div><div class="MsoNormal" style="font-family: arial; margin-bottom: 0.0001pt;"><span lang="EN">- Sementara itu, Analisa multivariat itu apa?</span></div><div class="MsoNormal" style="font-family: arial; margin-bottom: 0.0001pt;"><span lang="EN">Merupakan, Statistika method dimana digunakan untuk data yang multivariat kemudian diolah secara simultan.</span></div><div class="MsoNormal" style="font-family: arial; margin-bottom: 0.0001pt;"><span lang="EN"> </span></div><div class="MsoNormal" style="font-family: arial; margin-bottom: 0.0001pt;"><span lang="EN" style="font-size: 100%;"><br />
</span></div><div class="MsoNormal" style="font-family: arial; margin-bottom: 0.0001pt;"><span lang="EN">- Tunggu dulu, sebenarnya variat itu apa?</span></div><div class="MsoNormal" style="font-family: arial; margin-bottom: 0.0001pt;"><span lang="EN" style="font-size: 100%;"> Variat itu adalah gabungan dari beberapa variable. Tapi tidak jarang orang juga menamakan variat adalah variable.</span></div><div class="MsoNormal" style="font-family: arial; margin-bottom: 0.0001pt;"><span style="font-size: 100%;"><br />
</span></div><span lang="EN">Nah segitu aja deh kuliah multivariat -intro ^_^</span><br />
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<span lang="EN">Disadur dari <a href="http://ilma-ie.blogspot.com/2011/09/introduction-of-multivariat-basic.html">http://ilma-ie.blogspot.com/2011/09/introduction-of-multivariat-basic.html</a> </span><br />
<span lang="EN">oleh ilma fathnurfirda</span>Statistics Cafehttp://www.blogger.com/profile/02491182522761341918noreply@blogger.com7tag:blogger.com,1999:blog-7058004249720617071.post-23968361202510419372011-05-10T18:38:00.000-07:002011-05-10T18:38:00.126-07:00I HATE STATISTICSJust try to google "I hate Statistics", surprisingly there's a huge of it. Apparently, statistics is so popular (lol). There's even a group on facebook named <a href="http://www.facebook.com/...HATE-STATISTICS/217425661655">I HATE STATISTICS</a>. Wow!<br />
Let's take a look why those people do so.<br />
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<span style="color: blue;"> </span><cite class="fn" style="color: blue;">Jovan</cite><span class="comment-meta"> </span> </div><div class="comment-text">I, for one, hate statistics for the following reasons:<br />
- It’s pseudomathematics. It dresses up as a concise set of theories and methods, when these would more properly be referred to as cookbooks.<br />
- It’s simplistic. It gives a false sense of understanding about complex systems where no understanding exists. It prevents people from searching for mechanistic explanations that could indeed provide valuable insights.<br />
- It’s self-adulatory. Its practitioners have the courage to call every little possible way to plot data a “tool” or a “method”.<br />
- It’s too widespread. Most college programs that lack the most basic mathematics have their statistics courses (humanities and sciences), which helps spread misconceptions and misuse.<br />
(<a href="http://flowingdata.com/">http://flowingdata.com</a>)<br />
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<div style="color: blue;"><i>Lil_Fig_Newton</i> </div>So sleepy... three more chapters to read... I HATE stats. WORST SUBJECT EVER! Why oh why did I choose to take it the last semester of my senior year??? Who gives a shit about the null hypothesis???<br />
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I'm cycling between extreme bouts of sleepiness and horrible anxiety about my exam, which is in 4 hours (holy fuck, how did time go by so fast?!?!) I need to make a 66 on the final to pass. Please cross your fingers, say prayers, or what ever thing you do for good luck. I have to pass this class to graduate and it is my next to last final. OK, must study more now. More info to CRAM into my exhausted brain. <br />
(<a href="http://www.atforumz.com/">http://www.atforumz.com</a>)<br />
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<div style="color: blue;"><i>Paul Dalton</i></div> There- do you understand? I don’t for what it is worth, but I accept that it is true. I have to do that a lot in my work. I have never been really good with math, yet my work requires looking at statistics. Concepts like confidence intervals and power equations are beyond my ability to truly understand- but I look at them and use them in my work all the time. Is that a weakness for me as an treatment activist- probably. <br />
And that is why I hate statistics.<br />
(<a href="http://blogs.poz.com/">http://blogs.poz.com</a>)<br />
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<span class="byline"><span class="vcard author"></span></span><i style="color: blue;">Deb</i> <br />
I agree that most people think the field should be renamed "sadistics" but I am not 100% sure why it's so despised.<br />
(<a href="http://www.stat.columbia.edu/">http://www.stat.columbia.edu</a>)<br />
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<div class="comment-author vcard"><cite class="fn" style="color: blue;">Cheryl</cite><span style="color: blue;"> </span><span class="comment-meta"> </span> </div><div class="comment-text">Statistics does suck. It is useless garbage that I will NEVER use. I am 54 years old and I have NEVER used it at work or even running my own business for 18+ years, so what the hell do I need if for now? I have to take it to graduate with my degree.<br />
I bore two boys, raised them, I have undergone open heart surgery and I have NEVER experienced the level of frustration and pain as I have had in this statistics class. <br />
The textbook is POORLY written and the online venue? DON’T have anything to do with Pearson!<br />
I would rather eat glass, drive a pencil through my eye AND walk on coals then to put up with this crap.<br />
There has been nothing my whole life, that could not be figured out by using just addtion, subtraction, multiplying and dividing. The plus? No STUPID rules, that if this happens, then use this or if there is this do this. PLAAEEEEZE! Who thought this junk up????<br />
(<a href="http://flowingdata.com/">http://flowingdata.com</a>) </div><br />
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<div class="comment-author vcard"><cite class="fn" style="color: blue;">Patrick</cite><span style="color: blue;"> </span><span class="comment-meta"> </span> </div><div class="comment-text">I hate statistics for a number of reasons:<br />
- My intro professor was without a doubt the worst professor I have ever had. This was essentially intro to statistics for non-statisticians and she took powerpoint slides right from the textbook and threw them up on a screen. Needless to say, it was absolutely useless. Then, during the lab session, she was trying to teach us R without giving us a good background on the concepts. Thankfully, I found a book that barely got me through the class and gave me a great appreciation for some of the concepts. The worst professors are those who lecture for 90 minutes, then say “Any questions.” At which point you don’t even know where to start because s/he lost you in minute two and didn’t care. This was stats for me.<br />
(<a href="http://flowingdata.com/">http://flowingdata.com</a>)<br />
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<div class="comment-author vcard"><cite class="fn" style="color: blue;"><a class="url" href="" rel="external nofollow">Nathan Yau</a></cite><span style="color: blue;"> </span><span class="comment-meta"> </span> </div><div class="comment-text">actually all of my CS professors were pretty dynamic. It’s the projects I didn’t like :)</div>(<a href="http://flowingdata.com/">http://flowingdata.com</a>)</div><br />
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<div style="color: blue;"><i>Steve</i> </div>I may have mentioned it before, but statistics is <span style="font-style: italic;">the </span>worst subject ever to be inflicted on a student. It's even worse than maths.<br />
(<a href="http://thedeskinthecorner.blogspot.com/">http://thedeskinthecorner.blogspot.com</a>)<br />
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<div style="color: blue;"><i>~Alison</i> </div>I'm half way thru my online statistics course & I too hate it. It makes no sense to me. i can do the work & give them an answer, but I'm not really learning it. Thankfully only 2 tests left.<br />
It does really stink though. It's confusing to me. Maybe taking it online was not a good idea. it might hve made more sense if I had a teacher lecturing on the material.<br />
(<a href="http://allnurses.com/">http://allnurses.com</a>)<br />
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<div class="comment-author vcard"><cite class="fn" style="color: blue;">Jon Peltier</cite><span class="comment-meta"> </span> </div><div class="comment-text">I don’t think people dislike statistics because they are bad at math (though they may be bad at math).<br />
I don’t think the uncertainty is the reason, or the order it imposes.<br />
I think the major reason people dislike statistics is that it was poorly taught in whatever classes they took. Perhaps the instructor didn’t get it, or didn’t do the examples well.<br />
A related reason that people don’t like statistics is that any examples they ever saw were not relevant to something they understood or cared about.<br />
I wasn’t wild about the classroom statistics I had, but what I’ve learned since then has been interesting.<br />
(<a href="http://flowingdata.com/">http://flowingdata.com</a>) <br />
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</div><div style="color: blue;"><i>Tony</i></div>It sucks soo bad. I get headaches doing this crap (<a href="http://amplicate.com/hate/statistics">http://amplicate.com/hate/statistics</a>)<br />
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So, why do you think you should love statistics? :D <br />
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<div style="text-align: left;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiZ5Ck3FBRG-HCLpICEvpJ-gjQW4PerVFi5CVObF9z_OHwL1tH9Dllm0bvK1VS6kJ2QV7Mjq32Eg1q_h_urZchlO3AnElho-ek3kKdV9kWTWbkDkUBKxdDmbiwBnzgotR4tBSRfCpS5JcT_/s1600/i_love_statistics_t_shirt-p235548798422851909q6wh_400.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiZ5Ck3FBRG-HCLpICEvpJ-gjQW4PerVFi5CVObF9z_OHwL1tH9Dllm0bvK1VS6kJ2QV7Mjq32Eg1q_h_urZchlO3AnElho-ek3kKdV9kWTWbkDkUBKxdDmbiwBnzgotR4tBSRfCpS5JcT_/s200/i_love_statistics_t_shirt-p235548798422851909q6wh_400.jpg" width="200" /></a></div><br />
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</div>Statistics Cafehttp://www.blogger.com/profile/02491182522761341918noreply@blogger.com1tag:blogger.com,1999:blog-7058004249720617071.post-16632337376589469982011-05-08T20:30:00.000-07:002011-11-14T23:23:19.250-08:00How to Use the Likert Scale in Statistical AnalysisA Likert scale (pronounced /ˈlɪkərt/,[1] also /ˈlaɪkərt/) is a psychometric scale commonly used in questionnaires, and is the most widely used scale in survey research, such that the term is often used interchangeably with rating scale even though the two are not synonymous. When responding to a Likert questionnaire item, respondents specify their level of agreement to a statement. The scale is named after its inventor, psychologist Rensis Likert.[2]<br />
Sample question presented using a five-point Likert item<br />
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An important distinction must be made between a Likert scale and a Likert item. The Likert scale is the sum of responses on several Likert items. Because Likert items are often accompanied by a visual analog scale (e.g., a horizontal line, on which a subject indicates his or her response by circling or checking tick-marks), the items are sometimes called scales themselves. This is the source of much confusion; it is better, therefore, to reserve the term Likert scale to apply to the summated scale, and Likert item to refer to an individual item.<br />
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A Likert item is simply a statement which the respondent is asked to evaluate according to any kind of subjective or objective criteria; generally the level of agreement or disagreement is measured. Often five ordered response levels are used, although many psychometricians advocate using seven or nine levels; a recent empirical study[3] found that a 5- or 7- point scale may produce slightly higher mean scores relative to the highest possible attainable score, compared to those produced from a 10-point scale, and this difference was statistically significant. In terms of the other data characteristics, there was very little difference among the scale formats in terms of variation about the mean, skewness or kurtosis.<br />
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The format of a typical five-level Likert item is:<br />
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1. Strongly disagree<br />
2. Disagree<br />
3. Neither agree nor disagree<br />
4. Agree<br />
5. Strongly agree<br />
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Likert scaling is a bipolar scaling method, measuring either positive or negative response to a statement. Sometimes a four-point scale is used; this is a forced choice method[citation needed] since the middle option of "Neither agree nor disagree" is not available.<br />
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Likert scales may be subject to distortion from several causes. Respondents may avoid using extreme response categories (central tendency bias); agree with statements as presented (acquiescence bias); or try to portray themselves or their organization in a more favorable light (social desirability bias). Designing a scale with balanced keying (an equal number of positive and negative statements) can obviate the problem of acquiescence bias, since acquiescence on positively keyed items will balance acquiescence on negatively keyed items, but central tendency and social desirability are somewhat more problematic.<br />
Scoring and analysis<br />
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After the questionnaire is completed, each item may be analyzed separately or in some cases item responses may be summed to create a score for a group of items. Hence, Likert scales are often called summative scales.<br />
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Whether individual Likert items can be considered as interval-level data, or whether they should be considered merely ordered-categorical data is the subject of disagreement. Many regard such items only as ordinal data, because, especially when using only five levels, one cannot assume that respondents perceive all pairs of adjacent levels as equidistant. On the other hand, often (as in the example above) the wording of response levels clearly implies a symmetry of response levels about a middle category; at the very least, such an item would fall between ordinal- and interval-level measurement; to treat it as merely ordinal would lose information. Further, if the item is accompanied by a visual analog scale, where equal spacing of response levels is clearly indicated, the argument for treating it as interval-level data is even stronger.<br />
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When treated as ordinal data, Likert responses can be collated into bar charts, central tendency summarised by the median or the mode (but some would say not the mean), dispersion summarised by the range across quartiles (but some would say not the standard deviation), or analyzed using non-parametric tests, e.g. chi-square test, Mann–Whitney test, Wilcoxon signed-rank test, or Kruskal–Wallis test.[4] Parametric analysis of ordinary averages of Likert scale data is also justifiable by the Central Limit Theorem, although some would disagree that ordinary averages should be used for Likert scale data.<br />
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Responses to several Likert questions may be summed, providing that all questions use the same Likert scale and that the scale is a defendable approximation to an interval scale, in which case they may be treated as interval data measuring a latent variable. If the summed responses fulfill these assumptions, parametric statistical tests such as the analysis of variance can be applied. These can be applied only when more than 5 Likert questions are summed.[citation needed]<br />
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Data from Likert scales are sometimes reduced to the nominal level by combining all agree and disagree responses into two categories of "accept" and "reject". The chi-square, Cochran Q, or McNemar test are common statistical procedures used after this transformation.<br />
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Consensus based assessment (CBA) can be used to create an objective standard for Likert scales in domains where no generally accepted standard or objective standard exists. Consensus based assessment (CBA) can be used to refine or even validate generally accepted standards.<br />
Level of measurement<br />
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The five response categories are often believed to represent an Interval level of measurement. But this can only be the case if the intervals between the scale points correspond to empirical observations in a metric sense. In fact, there may also appear phenomena which even question the ordinal scale level. For example, in a set of items A,B,C rated with a Likert scale circular relations like A>B, B>C and C>A can appear. This violates the axiom of transitivity for the ordinal scale.<br />
Rasch model<br />
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Likert scale data can, in principle, be used as a basis for obtaining interval level estimates on a continuum by applying the polytomous Rasch model, when data can be obtained that fit this model. In addition, the polytomous Rasch model permits testing of the hypothesis that the statements reflect increasing levels of an attitude or trait, as intended. For example, application of the model often indicates that the neutral category does not represent a level of attitude or trait between the disagree and agree categories.<br />
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Again, not every set of Likert scaled items can be used for Rasch measurement. The data has to be thoroughly checked to fulfill the strict formal axioms of the model.<br />
Pronunciation<br />
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Rensis Likert, the developer of the scale, pronounced his name 'lick-urt' with a short "i" sound.[5][6] It has been claimed that Likert's name "is among the most mispronounced in [the] field."[7] Although many people use the long "i" variant ('lie-kurt'), those who attempt to stay true to Dr. Likert's pronunciation use the short "i" pronunciation ('lick-urt').<br />
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From Wikipedia, the free encyclopedia<br />
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The Likert scale is commonly used in survey research. It is often used to measure respondents' attitudes by asking the extent to which they agree or disagree with a particular question or statement. A typical scale might be "strongly agree, agree, not sure/undecided, disagree, strongly disagree." On the surface, survey data using the Likert scale may seem easy to analyze, but there are important issues for a data analyst to consider.<br />
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Instructions<br />
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1. Get your data ready for analysis by coding the responses. For example, let's say you have a survey that asks respondents whether they agree or disagree with a set of positions in a political party's platform. Each position is one survey question, and the scale uses the following responses: Strongly agree, agree, neutral, disagree, strongly disagree. In this example, we'll code the responses accordingly: Strongly disagree = 1, disagree = 2, neutral = 3, agree = 4, strongly agree = 5.<br />
2. Remember to differentiate between ordinal and interval data, as the two types require different analytical approaches. If the data are ordinal, we can say that one score is higher than another. We cannot say how much higher, as we can with interval data, which tell you the distance between two points. Here is the pitfall with the Likert scale: many researchers will treat it as an interval scale. This assumes that the differences between each response are equal in distance. The truth is that the Likert scale does not tell us that. In our example here, it only tells us that the people with higher-numbered responses are more in agreement with the party's positions than those with the lower-numbered responses.<br />
3. Begin analyzing your Likert scale data with descriptive statistics. Although it may be tempting, resist the urge to take the numeric responses and compute a mean. Adding a response of "strongly agree" (5) to two responses of "disagree" (2) would give us a mean of 4, but what is the significance of that number? Fortunately, there are other measures of central tendency we can use besides the mean. With Likert scale data, the best measure to use is the mode, or the most frequent response. This makes the survey results much easier for the analyst (not to mention the audience for your presentation or report) to interpret. You also can display the distribution of responses (percentages that agree, disagree, etc.) in a graphic, such as a bar chart, with one bar for each response category.<br />
4. Proceed next to inferential techniques, which test hypotheses posed by researchers. There are many approaches available, and the best one depends on the nature of your study and the questions you are trying to answer. A popular approach is to analyze responses using analysis of variance techniques, such as the Mann Whitney or Kruskal Wallis test. Suppose in our example we wanted to analyze responses to questions on foreign policy positions with ethnicity as the independent variable. Let's say our data includes responses from Anglo, African-American, and Hispanic respondents, so we could analyze responses among the three groups of respondents using the Kruskal Wallis test of variance.<br />
5. Simplify your survey data further by combining the four response categories (e.g., strongly agree, agree, disagree, strongly disagree) into two nominal categories, such as agree/disagree, accept/reject, etc.). This offers other analysis possibilities. The chi square test is one approach for analyzing the data in this way.<br />
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Read more: How to Use the Likert Scale in Statistical Analysis | eHow.com http://www.ehow.com/how_4855078_use-likert-scale-statistical-analysis.html#ixzz1LGrJsRUS<br />
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Opinion:<br />
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There's a huge debate ongoing in the social / behavioral sciences over whether Likert scales should be treated as ordinal or interval.<br />
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Count me as one who thinks it's OK to treat them as interval.<br />
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I would analyze the data both ways - with chi-square and with ANOVA, and see how it turns out - if the outcomes are the same, you're all set. If you get something different with each method, then you have something interesting...<br />
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Overall, you can treat the scales as interval and run methods that compare means, such as ANOVA. The scales are close enough to interval so that these methods shouldn't lead you astray.<br />
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Yes, Tukey would be fine for a post-hoc test. It's "middle-of-the-road" in terms of liberal/conservative (Fisher's LSD is liberal, Bonferroni is conservative).<br />
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In terms of how you would use chi-square, you could set up a comparison between the groups you want to contrast, and do the analysis on the frequency of each choice, between the groups (i.e., did one group choose "agree" more often than another group). Yes, it would be a chi-square test of independence. The contingency table could be set up with groups as rows, and scale items as 8 columns. The cells of the table would contain the response frequencies.<br />
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For chi-square post-hoc, use a simple comparison of two independent proportions with a z test.<br />
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You wouldn't necessarily report means with a chi-square analysis, since your interest is in comparing frequencies, but that's not to say you wouldn't do some sort of basic descriptive statistics comparison (means, medians, std dev, etc.)Statistics Cafehttp://www.blogger.com/profile/02491182522761341918noreply@blogger.com219tag:blogger.com,1999:blog-7058004249720617071.post-46456229488677248502011-05-05T20:32:00.001-07:002011-05-05T20:32:29.416-07:00Statistics for Management and Economics Answers<div class="line description"><div><strong><span style="font-size: small;">Introduction to statistics for the management and economics answers:</span></strong><br />
<div style="text-align: justify;"><strong><span style="font-size: small;"> </span></strong><span style="font-size: small;"><span style="font-size: x-small;">Statistics for the management and economics answers deals with the statistics mechanisms which is used to plot the data in a tabularized form and </span></span><span style="font-size: small;"><span style="font-size: x-small;">the term economics deals with the production and consumption, the data used in the economics are represented using the statistical methods so that we can manage the economical easily. In this article we deal with the statistics for the management and economics answers.</span></span><span style="font-size: small;"><span style="font-size: x-small;"></span></span><span style="font-size: small;"><span style="font-size: x-small;"></span></span></div></div><h2>Statistics for the Management and Economics Answers:</h2><span style="font-size: x-small;"><strong>Managing the data:</strong></span><br />
<span style="font-size: x-small;"> The economical data are represented using statistical methods like graphs, charts like tables, charts, graphs or in a standard format and also in finding the probability of the random variables using the different probability distributions like poisson,binomial we can manage the economical data.</span><br />
<div class="compoundfield-textarea"><div style="text-align: justify;"><span style="font-size: x-small;">The following are the points that represents the importance of statistics to economics, they are</span></div><ul style="text-align: justify;"><li><span style="font-size: x-small;">Quantitative expression of economic problem</span></li>
<li><span style="font-size: x-small;">Inter sectoral and inter temporal comparisons</span></li>
<li><span style="font-size: x-small;">working out cause and effect relationship</span></li>
<li><span style="font-size: x-small;">construction of economic theories and models</span></li>
<li><span style="font-size: x-small;">economic forecasting </span></li>
<li><span style="font-size: x-small;">Formulation of policies</span></li>
<li><span style="font-size: x-small;">economic equilibrium</span></li>
</ul></div><h2>Example Problems - Statistics for the Management and Economics Answers</h2><span style="font-size: x-small;">Some of the statistical methods to manage the data are given below,</span><br />
<span style="font-size: x-small;">Here we will see how the sample problems are solved using the different types of graphs like bar graph, histogram, pie- chart,line graph, scatter plot graphs.</span><br />
<span style="font-size: x-small;">Depends on the types of data, we can select the types of graphs.</span><br />
<span style="font-size: x-small;"><strong>Example problem 1- statistics for the management and economics answers</strong></span><br />
<span style="font-size: x-small;">Let us consider the following organised data given in the table, Manage the given organised data using statistical graphs.</span><br />
<span style="font-size: x-small;">The following table shows, the numbers of visitors in the bank in a week.Solve the organised data.</span><br />
<table border="2" style="height: 167px; width: 270px;"><tbody>
<tr><td><span style="font-size: x-small;">Days</span></td><td><span style="font-size: x-small;">Visitors</span></td></tr>
<tr><td><span style="font-size: x-small;">1</span></td><td><span style="font-size: x-small;">35</span></td></tr>
<tr><td><span style="font-size: x-small;">2</span></td><td><span style="font-size: x-small;">65</span></td></tr>
<tr><td><span style="font-size: x-small;">3</span></td><td><span style="font-size: x-small;">24</span></td></tr>
<tr><td><span style="font-size: x-small;">4</span></td><td><span style="font-size: x-small;">60</span></td></tr>
<tr><td><span style="font-size: x-small;">5</span></td><td><span style="font-size: x-small;">71</span></td></tr>
<tr><td><span style="font-size: x-small;">6</span></td><td><span style="font-size: x-small;">42</span></td></tr>
<tr><td><span style="font-size: x-small;">7</span></td><td><span style="font-size: x-small;">96</span></td></tr>
</tbody></table><span style="font-size: x-small;"><br />
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<span style="font-size: x-small;"><strong>Solution:</strong></span><br />
<span style="font-size: x-small;">The given data can be solved using the statistical graphs. The given days and number of visitors can be plotted using the bar graph where the x axis takes the days and y -axis takes the visitors range.</span><br />
<span style="font-size: x-small;"><img alt="Bar graph - statistics for the management and economics answers" height="361" src="http://image.wistatutor.com/content/feed/tvcs/dfrfretrttr22.PNG" title="Bar graph - statistics for the management and economics answers" width="619" /></span><br />
<span style="font-size: x-small;"><strong>Example problem 2 - statistics for the management and economics answers</strong></span><br />
<span style="font-size: x-small;">Calculate the value of the Poisson probability distribution in which the ,<span class="AM"><img src="http://www.tutorvista.com/js/jsMath/wysiwyg_asciimath/mimetex/mimetex.cgi?%5Cdisplaystyle%5Clambda" style="vertical-align: middle;" title="lambda" /></span> value is 4, x value is 7 and e = 2.718</span><br />
<span style="font-size: x-small;"><strong>Solution:</strong> </span><br />
<span style="font-size: x-small;"><strong>Step 1: Given:</strong></span><br />
<div style="padding-left: 60px;"><span style="font-size: x-small;"><span class="AM"><img src="http://www.tutorvista.com/js/jsMath/wysiwyg_asciimath/mimetex/mimetex.cgi?%5Cdisplaystyle%5Clambda" style="vertical-align: middle;" title="lambda" /></span> = 4</span></div><div style="padding-left: 60px;"><span style="font-size: x-small;">x = 7</span></div><span style="font-size: x-small;"><strong>Step 2: Formula:</strong></span><br />
<div style="padding-left: 60px;"><span style="font-size: x-small;">Poisson probability distribution = <sup> </sup><span class="AM"><img src="http://www.tutorvista.com/js/jsMath/wysiwyg_asciimath/mimetex/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B%7B%5Cleft%28%7B%7Be%7D%7D%5E%7B%7B-%5Clambda%7D%7D%5Cright%29%7D%7B%5Cleft%28%7B%5Clambda%7D%5E%7B%7Bx%7D%7D%5Cright%29%7D%7D%7D%7B%7B%7Bx%7D%21%7D%7D" style="vertical-align: middle;" title="((e^(-lambda))(lambda^x))/(x!)" /></span></span></div><span style="font-size: x-small;"><strong>Step 3: To find e</strong><span class="AM">:</span></span><br />
<div style="padding-left: 60px;"><span style="font-size: x-small;">e<sup>-4</sup> = (2.718)<sup>-4</sup> </span></div><div style="padding-left: 60px;"><span style="font-size: x-small;"> = 0.01831</span></div><span style="font-size: x-small;"><strong>Step 4: Solve:</strong></span><br />
<div style="padding-left: 60px;"><span style="font-size: x-small;"><span class="AM"><img src="http://www.tutorvista.com/js/jsMath/wysiwyg_asciimath/mimetex/mimetex.cgi?%5Cdisplaystyle%5Clambda" style="vertical-align: middle;" title="lambda" /></span> = 4</span></div><div style="padding-left: 60px;"><span style="font-size: x-small;"> x = 7</span></div><div style="padding-left: 60px;"><span style="font-size: x-small;"><span class="AM"><img src="http://www.tutorvista.com/js/jsMath/wysiwyg_asciimath/mimetex/mimetex.cgi?%5Cdisplaystyle%7B%5Clambda%7D%5E%7B%7Bx%7D%7D" style="vertical-align: middle;" title="lambda^x" /></span> = (4)<sup>7 </sup> = 16384</span></div><span style="font-size: x-small;"><strong>Step 4: Substitute:</strong></span><br />
<div style="padding-left: 60px;"><span style="font-size: x-small;"><span class="AM"><img src="http://www.tutorvista.com/js/jsMath/wysiwyg_asciimath/mimetex/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B%7B%5Cleft%28%7B%7Be%7D%7D%5E%7B%7B-%7B%5Clambda%7D%7D%7D%5Cright%29%7D%7B%5Cleft%28%7B%5Clambda%7D%5E%7B%7Bx%7D%7D%5Cright%29%7D%7D%7D%7B%7B%7Bx%7D%21%7D%7D" style="vertical-align: middle;" title="((e^-lambda)(lambda^x))/(x!)" /></span> = <span class="AM"><img src="http://www.tutorvista.com/js/jsMath/wysiwyg_asciimath/mimetex/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B%7B0.01831%7D%7B%5Cleft%28%7B16384%7D%5Cright%29%7D%7D%7D%7B%7B%7B7%7D%21%7D%7D" style="vertical-align: middle;" title="(0.01831(16384))/(7!)" /></span> </span></div><div style="padding-left: 60px;"><span style="font-size: x-small;"> = <span class="AM"><img src="http://www.tutorvista.com/js/jsMath/wysiwyg_asciimath/mimetex/mimetex.cgi?%5Cdisplaystyle%5Cfrac%7B%7B299.99104%7D%7D%7B%7B%7B5040%7D%7D%7D" style="vertical-align: middle;" title="299.99104/(5040)" /></span> </span></div><div style="padding-left: 60px;"><span style="font-size: x-small;"> <span class="AM"> </span> = 0.06</span></div><span style="font-size: x-small;"><strong>Result:</strong> Poisson probability Distribution = 0.06</span><br />
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</div>Statistics Cafehttp://www.blogger.com/profile/02491182522761341918noreply@blogger.com1tag:blogger.com,1999:blog-7058004249720617071.post-61236821759011680372011-05-05T20:32:00.000-07:002011-05-05T20:32:08.841-07:00Statistika : Kesederhanaan yang dapat membangun sebuah keputusan yang tepat<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiSlYJKya33lV0Hy1KWPpeirVPxZNg5EPi6wLjlJIUxQ728vCvSGjd7bMcMozp33y5_8gutNb-y3XDHe6WHAF2kG1WjZPmGFHd_ZfDUpJ78sR6inV9zMlN6adgAaAAwn2oO4p0W9GrD7Gy_/s1600/Kang+Daniel.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiSlYJKya33lV0Hy1KWPpeirVPxZNg5EPi6wLjlJIUxQ728vCvSGjd7bMcMozp33y5_8gutNb-y3XDHe6WHAF2kG1WjZPmGFHd_ZfDUpJ78sR6inV9zMlN6adgAaAAwn2oO4p0W9GrD7Gy_/s200/Kang+Daniel.png" width="99" /></a></div><br />
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By: Daniel Agustinus Nababan<br />
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<span class="fullpost"><span style="font-weight: bold;">Pendahuluan</span><br />
Fenomena berpikir tanpa berpikir atau yang lebih dikenal dengan BLINK sangat marak di dunia pemasaran. Pemasar mulai mencari insight yang membantu mereka dalam membentuk sebuah keputusan yang tepat untuk memecahkan masalah di dunia pemasaran. Decision Support System yang harus dimiliki pemasar harus benar-benar lengkap dan integrated system. Decision Support System itu tidak harus sampai kepada informasi intelligence tapi cukup hanya dalam ranah data sederhana dengan tampilan yang sederhana-pun, data itu mampu berbicara banyak dan merangsang pemasar untuk menjadikan sebuah program yang sukses dan berkelanjutan.<br />
Malcolm Galdwell dalam bukunya mengatakan bahwa kita perlu 10,000 hours untuk menjadi seorang ahli. Tapi bukan hanya semata-mata menghabiskan 10,000 jam tapi tidak melakukan apa-apa. Tapi bekerja dengan sekeras mungkin. Di dalam bekerja sekeras mungkin itu (extremely worked hard), itulah akan muncul sebuah tingkat intelegensi yang dapat memadukan beberapa data sederhana menjadi sebuah pengetahuan yang dapat ditindaklanjuti.<br />
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<span style="font-weight: bold;">Statistika dan Statistik</span><br />
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjTjPtHttNNhMYivtjQMqtIetwMWNgChovBd_KGeyzJRQVXGU1aJPLkrE2zg8L0BuOb9EF43H7ICF3VWERBzgplRsCa04nIX7QR6eh2JRiPiVTq42brFIJCtejurWJsPpBhrVQ_yEyKh_Ok/s1600/stat3.jpg"><img alt="" border="0" id="BLOGGER_PHOTO_ID_5595272931563729746" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjTjPtHttNNhMYivtjQMqtIetwMWNgChovBd_KGeyzJRQVXGU1aJPLkrE2zg8L0BuOb9EF43H7ICF3VWERBzgplRsCa04nIX7QR6eh2JRiPiVTq42brFIJCtejurWJsPpBhrVQ_yEyKh_Ok/s320/stat3.jpg" style="cursor: pointer; float: right; height: 235px; margin: 0pt 0pt 10px 10px; width: 214px;" /></a><br />
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Ketika mendengar kata statistik, orang-orang pasti akan cenderung berpendapat negative dibandingkan dengan pendapat positif. Statistik memang lebih dekat kepada sebuah kelompok data yang ribet, berupa baris dan kolom, deretan dan susunan angka-angka bahkan dengan kata “menyusahkan”-pun sangat dekat. Tapi di balik itu, apa yang bisa kita dapatkan dari statistik itu?<br />
Wikipedia menjelaskan bahwa statistik adalah data, informasi, atau hasil penerapan algoritma statistika pada suatu data. Sedangkan ilmu yang mempelajarinya adalah Statistika. Salah satu alat yang paling sederhana dalam tehnik analisis statistik adalah analisis deskriptif.<br />
Tehnik analisis statistik deskriptif adalah metode-metode yang berkaitan dengan pengumpulan dan penyajian suatu gugus data sehingga memberikan informasi yang berguna. Contoh statistika deskriptif yang sering muncul adalah, tabel, diagram, grafik, dan besaran-besaran lain. Dengan Statistika deskriptif, kumpulan data yang diperoleh akan tersaji dengan ringkas dan rapi serta dapat memberikan informasi inti dari kumpulan data yang ada.<br />
Hal inti menjadi sorotan utama dari statistika deskriptif. Bagaimana inti dari kumpulan data yang diambil menjadi sebuah system yang dapat mendukung kita dalam mengambil sebuah kesimpulan. Tabel, diagram dan grafik tidak menjadi bentuk transformasi dari tabulasi-tabulasi dan deret angka-angka yang ditampilkan dari analisis statistic yang dilakukan melalui software-software statistik yang ada.<br />
Ada beberapa hal yang perlu diperhatikan dalam transformasi data :<br />
1.Transformasi yang dimaksud mencitrakan angka dan data yang benar dan ada.<br />
2.Transformasi yang ditampilkan dapat meringkas dan berbicara lebih cepat.<br />
3.Transformasi yang terbentuk mempercepat kita memahami karakteristik yang terpola.<br />
4.Transformasi yang diwujudkan merangsang kita untuk lebih mengerti.<br />
Dari keempat hal itulah akan muncul sebuah BLINK yang menjadikan statistic itu berdaya guna dan dapat ditindaklanjuti. Kenyataan bahwa statistik yang sederhana itu semakin terwujud ketika grafik dan diagram itu menceritakan gejala dan karakteristik yang ada.<br />
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhHPZo5eFcOxc_fVu3cToRlJIbg9xhdfHZrmIGaIFBwXLNY4rUUbqC74ZYaF3I70bw38YrjN4CFTH5MAi3ECRYQDtpRVE2H6cBtuK4gDEbWYeKaDjqGlqRmtUulI_YETz7JtAJkDgk0Hw8v/s1600/stat2.jpg"><img alt="" border="0" id="BLOGGER_PHOTO_ID_5595272932306168978" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhHPZo5eFcOxc_fVu3cToRlJIbg9xhdfHZrmIGaIFBwXLNY4rUUbqC74ZYaF3I70bw38YrjN4CFTH5MAi3ECRYQDtpRVE2H6cBtuK4gDEbWYeKaDjqGlqRmtUulI_YETz7JtAJkDgk0Hw8v/s320/stat2.jpg" style="cursor: pointer; float: right; height: 194px; margin: 0pt 0pt 10px 10px; width: 259px;" /></a><br />
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<span style="font-weight: bold;">Kesederhanaan yang dapat membangun sebuah keputusan yang tepat</span><br />
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Melalui data-data dan grafik sederhana dapat muncul sebuah keputusan yang tepat. Bukan harus melalui alat statistic atau metoda analisis yang advance atau inferens bisa muncul keputusan yang tepat. Memang metoda analisis lanjutan tetap dibutuhkan untuk mempelajari lebih dalam, tapi dengan melihat gejala pusat yang tepat, sebenarnya kita bisa membuat keputusan yang tepat dengan tingkat kepercayaan yang kita yakini.<br />
Intinya adalah melihat dengan tepat sesuatu yang tepat. Mencermati statistic yang tepat dengan metoda yang tepat. Bukan semata-mata menampilkan grafik dan table yang hebat, namun bukan menggambarkan karakteristik yang ingin diteliti. <br />
Data atau statistic itu dibutuhkan oleh setiap orang. Dan statistic itu sekarang ada banyak dan berlimpah. Namun, apakah orang sudah menggunakannya dengan tepat? Apakah data yang tepat sudah berdaya guna atau masih menjadi tumpukan file yang hanya menunjukkan kumpulan data saja tanpa didayagunakan? Sesuatu yang besar itu tidak selalu datang dari sesuatu yang besar. Bisa saja dari sebuah hal yang kecil bahkan sederhana. Tapi, jika dengan cara yang tepat mengerjakan dengan tepat apa yang tepat itu akan menjadi besar dan berdayaguna.<br />
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj8BBaGBV3Nbo-LVB7Tdz5pA6JN446gD_SOjUpZbuSCF0G_pjHqjcsUb6hF3-xKZt26oxBjN_TRxFg8KaXT4FnGzqWE5caVs6aZtpqRt1WRJIliIGToVp8HBMIpJZilIrh6RKsZAEDnSTf9/s1600/lie+without+statistics.jpg"><br />
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<span class="post-icons"> </span>Statistics Cafehttp://www.blogger.com/profile/02491182522761341918noreply@blogger.com0tag:blogger.com,1999:blog-7058004249720617071.post-22241830230313546852011-05-05T01:44:00.000-07:002011-05-05T20:25:46.130-07:00Book Review: How to Lie with Statistics<u>How to Lie with Statistics</u>, by Darrel Huff, should be required reading for everyone. The cachet of numbers are used all the time in modern society. Usually to end arguments–after all, who can argue with “facts”? Huff shows how the same set of numbers can be tweaked to show three different outcomes, depending on where you start and what you use. The fundamental lesson I learned from this book is that mathematical calculation involves a whole set of conditions, and any number derived from such a calculation is meaningless without understanding those conditions.<br />
<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiQFK7FyjbeN9rSjIPY2_S0T3J4gYxSxj29X2nTlpgwcugdoT0xIsWFTQeFqzmPbJOU8MqjwuPAGRfEY-zRXQlKt7d407oXokwoxpvH_ECvejDIB_S1bjCJWI_WjZYrbD-xmyJstXRMG9MY/s1600/how-to-lie-with-statistics.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiQFK7FyjbeN9rSjIPY2_S0T3J4gYxSxj29X2nTlpgwcugdoT0xIsWFTQeFqzmPbJOU8MqjwuPAGRfEY-zRXQlKt7d407oXokwoxpvH_ECvejDIB_S1bjCJWI_WjZYrbD-xmyJstXRMG9MY/s320/how-to-lie-with-statistics.jpg" width="207" /></a></div>He also mentions that colleagues have told him that the flurry of meaningless statistics is due to incompetence–he dispatches this argument with a simple query: “Why, then, do the numbers almost always favor the person quoting them?” Huff also provides five questions (not unlike the <a href="http://www.imdb.com/title/tt0364725/quotes">five d’s</a> of <a href="http://www.dodgeballmovie.com/">dodgeball</a>) for readers to ask, when confronted with a statistic:<br />
1. Who says so?<br />
2. How does he know?<br />
3. What’s missing?<br />
4. Did somebody change the subject?<br />
5. Does it make sense?<br />
All this is wrapped up in a book with simple examples (no math beyond arithmetic, really) and quaint 1950s prose. In addition humor runs from the beginning (the dedication is “To my wife with good reason”) to the end (on page 135, Huff says “Almost anybody can claim to be first in <em>something</em> if he is not too particular what it is”). This book is well worth a couple hours of your time.<br />
<br />
source: <a href="http://www.mooreds.com/">www.mooreds.com</a>Statistics Cafehttp://www.blogger.com/profile/02491182522761341918noreply@blogger.com0tag:blogger.com,1999:blog-7058004249720617071.post-15757020592704255002011-04-30T01:01:00.000-07:002011-05-05T20:24:40.799-07:00Chris Grayling use of crime statistics 'mislead' public<div class="first"><b>Shadow home secretary Chris Grayling has been accused of misleading the public in his use of crime statistics.</b></div>The Tories have said data shows a big rise in violent crime during Labour's time in government - but the way the figures were compiled changed in 2002. <br />
Now the chairman of the UK Statistics Authority has told Mr Grayling his statements are "likely to damage public trust in official statistics". <br />
But Mr Grayling said the Tories would continue to argue that crime had risen. <br />
Home Secretary Alan Johnson has said his opposite number should apologise. <br />
<b>'Likely to mislead'</b><br />
Mr Grayling had to defend his position on Wednesday after the Conservatives sent the figures to activists in constituencies throughout England and Wales in an effort to demonstrate the government's failure on law and order.<br />
<table align="right" border="0" cellpadding="0" cellspacing="0" style="width: 231px;"><tbody>
<tr> <td width="5"><img alt="" border="0" height="1" hspace="0" src="http://newsimg.bbc.co.uk/shared/img/o.gif" vspace="0" width="5" /></td> <td class="sibtbg"> <div><div class="mva"><img alt="" border="0" height="13" src="http://newsimg.bbc.co.uk/nol/shared/img/v3/start_quote_rb.gif" width="24" /> <b>I must take issue with what you said yesterday about violent crime statistics, which seems to me likely to damage public trust in official statistics</b> <img align="right" alt="" border="0" height="13" src="http://newsimg.bbc.co.uk/nol/shared/img/v3/end_quote_rb.gif" vspace="0" width="23" /><br clear="all" /> </div></div><div class="mva"><div>Sir Michael Scholar's letter to the Conservatives</div></div></td> </tr>
</tbody></table>The BBC's home editor Mark Easton said the method of recording violent crime had changed in 2002, making the figures for the periods before and after that date non-comparable. <br />
Now Sir Michael Scholar, chairman of the UK Statistics Authority, has written to Mr Grayling saying: "I do not wish to become involved in political controversy, but I must take issue with what you said yesterday about violent crime statistics, which seems to me likely to damage public trust in official statistics." <br />
In notes attached to the letter, the statistics authority said it regarded "a comparison, without qualification, of police-recorded statistics between the late 1990s and 2008/09 as likely to mislead the public". <br />
The authority said the British Crime Survey (BCS), an annual questionnaire of 46,000 people, indicated there had been a big fall in violent crime since 1995. <br />
It said the BCS was the most reliable way of assessing the trend, because it was "not affected by changes in reporting, police recording and local policing activity, and has been measuring crime in a consistent way since the survey began in 1981". <br />
Responding to Sir Michael's letter, Mr Grayling told the BBC he was "quite happy to reflect changes in methodology when talking about these figures in the future".<br />
<table align="right" border="0" cellpadding="0" cellspacing="0" style="width: 231px;"><tbody>
<tr> <td width="5"><img alt="" border="0" height="1" hspace="0" src="http://newsimg.bbc.co.uk/shared/img/o.gif" vspace="0" width="5" /></td> <td class="sibtbg"> <div class="sih">MARK EASTON'S UK </div><div class="o"><img alt="Letter by Sir Michael Scholar" border="0" height="66" hspace="0" src="http://newsimg.bbc.co.uk/media/images/47245000/jpg/_47245258_scholar_grayling03.jpg" vspace="0" width="226" /> </div><div class="o"><img alt="" border="0" height="1" hspace="0" src="http://newsimg.bbc.co.uk/nol/shared/img/v3/inline_dashed_line.gif" vspace="2" width="226" /><br />
</div><div class="miiib"><div class="arr"><a class="" href="http://www.bbc.co.uk/blogs/thereporters/markeaston/2010/02/grayling_crime_stats.html">See the letter in full and comment on Mark Easton's blog</a> </div></div></td> </tr>
</tbody></table>But he insisted: "The reality is these figures actually reflect real crimes, reported to real police stations, by real people. <br />
"And the reality is however you caveat these figures, whatever qualifications you make about changes to the recording methods, they show a big increase in violent crime over the past decade and we are going to carry on saying that." <br />
He added that he continued to believe the BCS, as a measure of crime, was "highly flawed". <br />
<b>'Selective'</b><br />
The home secretary claimed the Conservatives had "plenty of form" when it came to the use of statistics for political ends. <br />
"Now it has been confirmed officially that they have continually misled the public about crime," Mr Johnson said. <br />
This is not the first time the statistics body has taken politicians to task for their use and interpretation of crime-related figures. <br />
In 2008, it criticised Labour ministers for releasing what it said was "premature and selective" data about hospital admissions for knife wounds in certain parts of the country. The then Home Secretary Jacqui Smith apologised for the early release.<br />
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Source: <a href="http://news.bbc.co.uk/">http://news.bbc.co.uk </a>Statistics Cafehttp://www.blogger.com/profile/02491182522761341918noreply@blogger.com0tag:blogger.com,1999:blog-7058004249720617071.post-40561053411671740372011-04-26T21:08:00.000-07:002011-12-02T21:08:53.480-08:00Statistics Can Be Misleading<span style="font-family: Abadi MT Condensed;">A very interested information about stats, here is:<i><br />
</i></span><br />
<br />
<span style="font-family: Abadi MT Condensed;"><i>Author:</i> Lori Alden</span> <br />
<span style="font-family: Abadi MT Condensed;"><i>Audience</i>: High school and college economics students</span> <br />
<span style="font-family: Abadi MT Condensed;"><i>Summary</i>: </span><span style="font-family: Arial; font-size: x-small;">With this series of 12 puzzles, you can help your students become more discriminating consumers of economic statistics. </span> <br />
<span style="font-family: Abadi MT Condensed;"><i>Procedure</i>: Each of the following problems shows one or more misleading statistics. See if your students can figure out why they're misleading.</span> <br />
<span style="font-family: Abadi MT Condensed;"><span style="font-size: 12pt;">1</span></span><span style="font-size: 12pt;"><span style="font-family: Abadi MT Condensed;">. The following statistics suggest that 16-year-olds are safer drivers than people in their twenties, and that octogenarians are very safe. Is this true?</span></span><br />
<div align="center"><img border="0" height="468" src="http://www.econoclass.com/images/stataccidents.gif" width="487" /></div><i><span style="font-size: 12pt;"><span style="font-family: Abadi MT Condensed;">Solution: No. As the following graph shows, the reason 16-year-old and octogenarians appear to be safe drivers is that they don't drive nearly as much as people in other age groups. </span></span> </i><br />
<img border="0" height="452" src="http://www.econoclass.com/images/stataccidents2.gif" width="486" /><br />
<span style="font-family: Abadi MT Condensed;">2. On November 13, 2000, Newsweek published the following poll results:</span><br />
<div align="center"><span style="font-family: Abadi MT Condensed;"><img border="0" height="392" src="http://www.econoclass.com/mislea13.jpg" width="271" /></span></div><span style="font-size: 12pt;"><span style="font-family: Abadi MT Condensed;">Since 9% said that Nader was the only candidate worth voting for, one would have expected him to get at least 9% of the vote in the 2000 election. He only got about 3%. What happened?</span></span><br />
<i><span style="font-size: 12pt;"><span style="font-family: Abadi MT Condensed;">Solution:</span></span></i><br />
<div align="center"><img border="0" height="336" src="http://www.econoclass.com/images/statnader.gif" width="285" /></div><i><span style="font-family: Abadi MT Condensed;">There was a biased statistic because the <span style="color: blue;">sample</span> wasn't randomly drawn from the <span style="color: cyan;">population</span>. A disproportionate number of Nader supporters participated in the poll in order to make him appear more viable as a candidate.</span></i><br />
<span style="font-family: Abadi MT Condensed;">3. Consider these complaints about airlines published in US News and World Report on February 5, 2001:</span><br />
<div align="center"><span style="font-family: Abadi MT Condensed;"><img border="0" height="226" src="http://www.econoclass.com/mislea15.gif" width="234" /></span></div><span style="font-size: 12pt;"><span style="font-family: Abadi MT Condensed;">Can we conclude that United, American, and Delta are the worst airlines and Alaska, Southwest, and Continental are the best?</span></span> <i><span style="font-size: 12pt;"><span style="font-family: Abadi MT Condensed;">Solution: No. The airlines that had the most complaints also had the most passengers. As the following graph shows, rates and percentages are often more informative than raw numbers.</span></span></i><br />
<div align="center"><img border="0" height="226" src="http://www.econoclass.com/images/statplanes.gif" width="354" /></div><span style="font-family: Abadi MT Condensed;">4. The following statistics about motorcycle helmet use seem to suggest that helmets cause more injuries and fatalities. Is it really safer to go without helmets?</span><br />
<div align="center"><img border="0" height="148" src="http://www.econoclass.com/images/statcycles.gif" width="423" /></div><span style="font-family: Abadi MT Condensed;">Source: Motorcycle Statistical Annual, Motorcycle Industry Council, Inc., 1994, as reported on http://www.bikersrights.com/statistics/stats.html.</span><br />
<i><span style="font-family: Abadi MT Condensed;">Solution: Correlation doesn't prove causation. The statistics suggest that helmets cause accidents and fatalities, but it's possible that a high number of motorcycle accidents and fatalities in high-risk states caused them to adopt mandatory helmet laws. </span></i><br />
<span style="font-family: Abadi MT Condensed;">5. This clipping from US News and World Report on 1/29/01 suggests that Alaskans are terrible parents. Is this true?</span><br />
<div align="center"><span style="font-family: Abadi MT Condensed;"><img border="0" height="423" src="http://www.econoclass.com/mislea20.gif" width="237" /></span></div><i><span style="font-size: 12pt;"><span style="font-family: Abadi MT Condensed;">Solution: The difference in the abuse rates probably stems from different definitions for abuse in the various states. For example, Alaska (the "worst" state) says that a child is abused if his or her health or welfare is harmed or threatened. Pennsylvania (the "best" state) defines it as a recent act or failure to act.</span></span></i><br />
<span style="font-size: 12pt;"><span style="font-family: Abadi MT Condensed;">6. Columnist George Will wrote in the Washington Post in 1993 that "... the 10 states with the lowest per pupil spending included four — North Dakota, South Dakota, Tennessee, Utah — among the 10 states with the top SAT scores ... New Jersey has the highest per pupil expenditures, an astonishing $10,561… [Its] rank regarding SAT scores? Thirty-ninth." </span> </span><br />
<span style="font-size: 12pt;"><span style="font-family: Abadi MT Condensed;">This negative correlation between spending per pupil and SAT performance seems to be borne out by this graph:</span></span><br />
<div align="center"><img border="0" height="359" src="http://www.econoclass.com/images/statSAT3.gif" width="342" /></div><span style="font-family: Abadi MT Condensed;">And by this one:</span><br />
<div align="center"><img border="0" height="344" src="http://www.econoclass.com/images/statSAT2.gif" width="355" /></div><span style="font-size: 12pt;"><span style="font-family: Abadi MT Condensed;">Does this mean that spending more on education makes students worse off?</span></span><br />
<i><span style="font-size: 12pt;"><span style="font-family: Abadi MT Condensed;">Solution: The results are more likely due to differing SAT participation rates in the states (Colleges in North Dakota and other states require the ACT rather than the SAT for college admissions). The students who take the SAT in North Dakota include many who plan to apply to elite out-of-state colleges.</span></span></i><br />
<div align="center"><img border="0" height="132" src="http://www.econoclass.com/images/statSAT4.gif" width="377" /></div><i><span style="font-family: Abadi MT Condensed;">This caused a sampling bias, since the sample wasn't representative of the population.</span></i><br />
<span style="font-family: Abadi MT Condensed;">7. Researchers (Arthur Kellermann et. al., "Gun Ownership as a Risk Factor for Homicide in the Home," The New England Journal of Medicine, October 7, 1993, pp. 1084-1091), found that gun owners are 2.7 times more likely to be murdered than non-owners. Does this mean it's safer to not have guns in the house?</span><br />
<i><span style="font-family: Abadi MT Condensed;">Solution: Perhaps, but correlation does not imply causation. It may be true that guns cause murders, but it might also be true that having a greater risk of being murdered causes people to own guns.</span></i><br />
<span style="font-family: Abadi MT Condensed;">8. "The best public schools offer a more challenging curriculum than most private schools." Are public schools therefore better than private schools?</span><br />
<i><span style="font-family: Abadi MT Condensed;">Solution: We're being asked to compare apples with oranges: the <u> best</u> public schools versus <u> most </u> private schools.</span></i><br />
<span style="font-family: Abadi MT Condensed;">9. "Fluoride consumption by human beings increases the general cancer death rate. …. [P]eople in fluoridated areas have a higher cancer death rate than those in non-fluoridated areas." Should fluoridation be prohibited?</span><br />
<i><span style="font-family: Abadi MT Condensed;">Solution: Affluent areas are more likely to have fluoridation and they're also more likely to have older populations who are more likely to get cancer.</span></i><br />
<span style="font-family: Abadi MT Condensed;">10. Can we conclude from the following diagram that it's safer to drive while under the influence?</span><br />
<div align="center"><span style="font-family: Abadi MT Condensed;"> </span><img border="0" height="260" src="http://www.econoclass.com/images/statdrivers.gif" width="384" /></div><i><span style="font-family: Abadi MT Condensed;">Solution: No. <span style="font-family: "Times New Roman"; font-size: 12pt;">Drunk drivers have a fatality risk 7.66 times the norm, while non-drunk drivers have a risk only about .6 of the norm. Only a very small percentage of drivers in New York City drive while under the influence, but they account for a disproportionate number of accidents.</span></span></i><br />
<span style="font-size: 12pt;"><span style="font-family: Abadi MT Condensed;">11. The <a href="http://www.bls.gov/opub/ted/1998/Oct/wk3/art05.htm">Monthly Labor Review</a> published the following data, showing how earnings vary with education:</span></span><br />
<div align="center"><img border="0" height="210" src="http://www.econoclass.com/images/statearnings.gif" width="302" /></div><span style="font-size: 12pt;"><span style="font-family: Abadi MT Condensed;">Can we conclude that getting a bachelor's degree will increase your earnings by almost $13,000 a year?</span></span><br />
<i><span style="font-size: 12pt;"><span style="font-family: Abadi MT Condensed;">Solution: Not necessarily. Intelligence and drive also explain the differences in earnings, people with intelligence and drive are more likely to go to college.</span></span></i><br />
<span style="font-size: 12pt;"><span style="font-family: Abadi MT Condensed;">12. Allen Hershkowitz, senior scientist with the Natural Resources Defense Council, wrote that "a well-run curbside recycling program can cost anywhere from $50 to more than $150 per ton of materials collected. Typical trash collection and disposal programs, on the other hand, cost anywhere from $70 to more than $200 per ton." Does recycling save money?</span></span><br />
<i><span style="font-size: 12pt;"><span style="font-family: Abadi MT Condensed;">Solution: Hershkowitz asks us to compare apples with oranges: a <u> well-run</u> curbside recycling program with <u> typical</u> trash collection and disposal programs.</span></span></i><br />
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<span style="color: grey; font-family: Abadi MT Condensed;">© Lori Alden, 2005-7. All rights reserved. You may download the content, provided you only use the content for your own personal, non-commercial use. Lori Alden reserves complete title and full intellectual property rights in any content you download from this web site. Except as noted above, any other use, including the reproduction, modification, distribution, transmission, republication, display, or performance, of the content on this site is strictly prohibited.</span><br />
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<span style="color: grey; font-family: Abadi MT Condensed;">source: http://www.econoclass.com </span>Statistics Cafehttp://www.blogger.com/profile/02491182522761341918noreply@blogger.com0tag:blogger.com,1999:blog-7058004249720617071.post-21632712413632773922011-04-26T00:57:00.000-07:002011-05-05T20:23:19.043-07:00About Crime Statistics<span class="Note byline trigger">By Kirk Brown<span class="about">, eHow Contributor</span></span><div data-profile="AuthorProfileContainer"><div><div></div></div><br />
<div class="footerShare"><div class="FLC"><div class="facebookLike" style="background: none repeat scroll 0% 0% transparent;"></div></div></div><figure class="Thumbnail articlePhoto"> <figcaption class="Note caption"><br />
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<div class="intro" id="intelliTxt">When crimes such as burglaries, robberies or homicides are reported, this information goes into statistical databases that are utilized for a number of purposes. Crime statistics are often used to measure the safety of a specific area such as a neighborhood, city, state or nation. Crime statistics also help <a class="StrongLink" href="http://www.ehow.com/legal/">law</a> enforcement officials set planning and budgeting priorities. In addition, lawmakers rely on these figures in setting sentencing guidelines and devising programs to aid crime victims.</div><section class="Module body FLC"> <section> <ol class="generic" id="intelliTxt"><li class="section"> <h2 class="header Heading3">Function</h2><ul><li class="step"> <div class="stepMeat"><div itemprop="step">In order to better understand and control crime, accurate counts of its occurrence are needed. Crime statistics represent the recorded incidence of criminal behavior. These figures are typically compiled as uniform data on offenses and offenders derived from records of criminal justice agencies. Official crime statistics are published annually or periodically in relatively standard formats. The federal government's Bureau of Justice Statistics is the primary source of crime statistics in the United States.</div></div></li>
</ul><h2 class="header Heading3">History</h2><ul><li class="step"> <div class="stepMeat"><div itemprop="step">Crime statistic collection emerged in the early 19th century to measure whether criminal activity in certain areas was increasing or decreasing. France began systematically collecting national judicial statistics on prosecutions and convictions in 1825. Beginning in 1857, Great Britain was the first nation to systematically collect police data. In 1920, American criminologist August Vollmer proposed a national bureau of criminal records. The International Association of Chiefs of Police acted on this suggestion in 1927 by developing a plan for a national system of police statistics that would include known offenses and arrests collected from local police departments in each state. The Federal Bureau of Investigation became the clearinghouse for these statistics and published the first of its now-annual Uniform Crime Reports in 1931.<br />
To meet a growing need for more flexible, in-depth data, the FBI has supplemented its Uniform Crime Reports in recent years with what's known as the National Incident-Based Reporting System. Another relatively new wrinkle is the National Crime Victimization Survey, which collects data on the frequency, characteristics and consequences of criminal victimization from a nationally representative sample of 76,000 households.</div></div></li>
</ul><h2 class="header Heading3">Types</h2><ul><li class="step"> <div class="stepMeat"><div itemprop="step">The most frequently referenced classifications of criminal statistics are violent crimes and property crimes. Violent crimes include homicide, rape, robbery and assault. Property crimes consist of burglaries, thefts and motor <a class="StrongLink" href="http://www.ehow.com/cars/">vehicle</a> thefts.</div></div></li>
</ul><h2 class="header Heading3">Time Frame</h2><ul><li class="step"> <div class="stepMeat"><div itemprop="step">During 2007, an estimated 1.4 million violent crimes occurred in the United States, according to the FBI's Uniform Crime Reporting Program. This total represented a .7 percent decrease from the prior year. Aggravated assault was the most commonly reported violent crime, accounting for nearly 61 percent of the overall tally.<br />
Also in 2007, there were an estimated 9.8 million property crime offenses in the United States., which was 1.4 percent less than 2006. The 2007 property crime total resulted in an estimated $17.6 billion in losses.</div></div></li>
</ul><h2 class="header Heading3">Prevention/Solution</h2><ul><li class="step"> <div class="stepMeat"><div itemprop="step">One goal of gathering and publishing crime statistics is to determine where offenses are taking place so officials can take action to combat the problem. Crime statistics may prompt police to increase patrols in specific crime-prone areas. The same statistics also might lead government agencies to offer tax incentives to <a class="StrongLink" href="http://www.ehow.com/business/">businesses</a> in hopes of creating more commerce and providing additional jobs in these areas as a deterrent to crime.</div></div></li>
</ul></li>
</ol><div style="background-color: transparent; border: medium none; color: black; overflow: hidden; text-align: left; text-decoration: none;">Source: <a href="http://www.ehow.com/">www.ehow.com</a></div></section></section></div>Statistics Cafehttp://www.blogger.com/profile/02491182522761341918noreply@blogger.com0tag:blogger.com,1999:blog-7058004249720617071.post-45973548339231030152011-04-06T23:47:00.000-07:002011-05-05T20:24:01.752-07:00Is GDP An Obsolete Measure of Progress?<div style="font-family: "Trebuchet MS",sans-serif; text-align: justify;"><span style="font-size: small;">Seperti judulnya, pertanyaan inilah yang akan dijawab dalam artikel ini. Ada ukuran lain, selain GDP (PDB) atau usia harapan hidup, yang lebih dapat mendeskripsikan ukuran kemakmuran suatu negara, yaitu Happy Planet Index (HPI) atau mungkin kita lebih familiar dengan istilah Indeks Kebahagiaan. HPI mengkombinasikan metrik ekonomi dengan ukuran-ukuran lain yang bersifat subjektif, seperti kepuasan hidup. Pada tahun 2007 <a href="http://www.frontier.co.id/">perusahaan riset Frontier</a> telah melakukan metode riset ini. Metode ini sangat masuk akal dan tentu saja tidak dapat dipungkiri, karena apa artinya jika secara angka pertumbuhan ekonomi meningkat, tetapi setiap warga negara tidak merasa aman di negaranya sendiri. Apa artinya jika rata-rata usia harapan hidup di suatu negara meningkat, tetapi selama masa hidupnya itu mereka tidak menikmati kehidupannya. Karena itu, sudah semestinya data-data pemerintah didukung oleh data kualitatif untuk melengkapi data kuantitatif, yang tentunya menunjang dalam pengambilan keputusan. Perhitungannya? Sangat yakin kalau analisa statistik yang digunakan, tentunya akan berbicara lebih. </span></div><br />
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<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjGogGw16IcMW0LBnSQBxdoPvrM4bA4Wy_AyMe4IzAFN8lFyuHeUUGc4FypMKWgRlrbobDxZ1vrKRW28Y8WKvhjw3rx-lvjSMgEEAX8miEEFpBHiRSAnr52GdyhgrazkbRRRW8gwf2dcYYX/s1600/GDP.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="181" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjGogGw16IcMW0LBnSQBxdoPvrM4bA4Wy_AyMe4IzAFN8lFyuHeUUGc4FypMKWgRlrbobDxZ1vrKRW28Y8WKvhjw3rx-lvjSMgEEAX8miEEFpBHiRSAnr52GdyhgrazkbRRRW8gwf2dcYYX/s320/GDP.png" width="320" /></a></div>Chris Hondros / Getty<br />
<div style="text-align: justify;">Since last summer the nation's Gross Domestic Product (GDP) has gone up — indeed, it grew at a surprising 5.7% rate in the 4th quarter — seeming to confirm what we've been hearing: the recession is officially over. But wait — foreclosure and unemployment rates remain high, and food banks are seeing record demand. Could it be that the GDP, that gold standard of economic data, might not be the best way to gauge a nation's relative prosperity? </div><div style="text-align: justify;"><br />
</div><div style="text-align: justify;">Since it became the prime economic indicator during the Second World War (to monitor war production) many have criticized policy-makers' reliance on the GDP — and proposed substitute measures. For example, there is the Human Development Index (HDI), used by the UN's Development Programme, which considers life expectancy and literacy as well as standard of living as determined by GDP. And the Genuine Progress Indicator, which incorporates aspects of social welfare such as income equity, pollution, and access to health care. In the international community, perhaps the biggest nudge has come from French President Nicolas Sarkozy, who commissioned a report by marquee-name economists, including Nobel laureates Joseph Stiglitz and Amartya Sen, to find alternatives to what he calls "GDP fetishism".</div><div style="text-align: justify;"><br />
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</div><div style="text-align: justify;">What exactly have we been fetishizing? Basically, market activity and growth. The GDP, generally expressed as a per-capita figure and often adjusted to reflect purchasing power, represents the market value of good and services produced within a nation's boundaries. Sounds reasonable. Until we consider what it doesn't measure: the general progress in health and education, the condition of public infrastructure, fuel efficiency, community and leisure. </div><div style="text-align: justify;">"It's a narrow calculation of cash flow," says Hazel Henderson, President of Ethical Markets Media (USA and Brazil) and who co-developed the Calvert-Henderson Quality of Life Indicators, which unbundles, rather than averages, 12 indicators. "Because it's averaged, the GDP mystifies and masks the gap between rich and poor. I don't think there's ever been such a large disconnect between the GDP and what ordinary people are experiencing." (See TIME's 2009 Person of the Year: Federal Reserve Chairman Ben Bernanke.)</div><div style="text-align: justify;">As an example of how what's good for the GDP is not always good for the individual, take health care: rising costs may be tough on families, but it boosts the GDP. </div><div style="text-align: justify;">"The GDP is a truly terrible measure of things that really matter," says James Gustave (Gus) Speth, Distinguished Senior Fellow at Demos, a public policy research and advocacy organization based in New York. "Finally, there's a broad consensus on this point. For the first time there's a chance that this concern will move out of academic and research circles and become a real policy question." </div><div style="text-align: justify;">Speth notes the seemingly paradoxical relationship between the growth rate (GDP) and decline in employment. "It takes enormous GDP growth to get jobs," he says. "It focuses us as a nation on a fool's errand." </div><div style="text-align: justify;">One new calculation that's been attracting attention is the Happy Planet Index (HPI), which combines economic metrics with indicators of well-being, including subjective measures of life satisfaction, which have become quite sophisticated (HPI uses data from Gallup, World Values Survey, and Ecological Footprint). The HPI assesses social and economic well-being in the context of resources used, looking at the degree of human happiness generated per quantity of environment consumed. The HPI metric was driven in part by the recognition that the environmental costs of economic growth must be figured into standard-of-living reports. <span class="see"><a href="http://www.time.com/time/specials/packages/article/0,28804,1945379_1944446,00.html" target="_blank">(See the worst business deals of 2009.)</a></span></div><div style="text-align: justify;">"The GDP suited a different era and now we need a metric for our times," says Nic Marks, a Fellow at the London-based New Economics Foundation, and founder of its Centre for Well-Being. "During World War II production was important. After the war was the need for rebuilding. We're way past that. We need to account for our ecological footprint and see how we're operating on the planet. The GDP is often precisely wrong in that it's not measuring progress, just the making of stuff. The HPI is striving to measure a better future." One appeal of the GDP, says Marks, has been that it presents a simple message: up is "good"; down is "bad." "HPI is trying to mirror that simplicity, using one number as a headline indicator." </div><div style="text-align: justify;">In terms of what the world wants measured, it seems the HDI and HPI have it over the GDP. For its report "International Public Opinion on Measuring National Progress: 2007" GlobeScan, a research firm based in Canada and London, surveyed 1,000 people in each of 10 countries not including the U.S.. When asked whether health, social and environmental status should figure into measures of national progress as much as economic data, between 70% (Russia) and 86% (France) agreed. "It's common sense and matches their experience," says Hazel Henderson, whose firm commissioned the study. "People know there is much valuable in their lives besides what can be expressed in monetary terms." </div><div style="text-align: justify;">The matter of how a nation measures performance is far from trivial, says Gus Speth, particularly at a time when environment sustainability is on many people's minds. He observes: "You tend to get what you measure, so we'd better measure what we want." In other words, to a certain extent we are what we count. <span class="see"><a href="http://www.time.com/time/photogallery/0,29307,1677033,00.html" target="_blank">(See pictures of the stock market crash of 1929.)</a></span></div><div style="text-align: justify;">For Nic Marks, the key shift introduced by the HPI is its "move away from measuring production and toward measuring consumption. The HPI serves as a signpost pointing more toward a society we want to live in — the delivery of good lives rather than the delivery of more goods." </div><div style="text-align: justify;">So how does the U.S. fare in HPI terms? Not so good. It sits pretty far down the list at 114. The U.K. is 74, behind Germany, Italy and France. Topping the chart is Costa Rica, which has long life expectancy, high life satisfaction, and a per capita ecological footprint one-fourth the size of the U.S. </div><div style="text-align: justify;">As Gus Speth explains it: "We [in the U.S. are] chewing up a lot of environment for not much happiness.</div><br />
Sumber: <a href="http://www.time.com/">http://www.time.com</a>Statistics Cafehttp://www.blogger.com/profile/02491182522761341918noreply@blogger.com0