{"id":2531,"date":"2023-07-21T21:07:56","date_gmt":"2023-07-21T21:07:56","guid":{"rendered":"https:\/\/statorials.org\/id\/korelasi-di-airlock\/"},"modified":"2023-07-21T21:07:56","modified_gmt":"2023-07-21T21:07:56","slug":"korelasi-di-airlock","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/korelasi-di-airlock\/","title":{"rendered":"Cara menghitung korelasi di sas (dengan contoh)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">Salah satu cara untuk mengukur hubungan antara dua variabel adalah dengan menggunakan <a href=\"https:\/\/statorials.org\/id\/koefisien-korelasi-pearson-1\/\" target=\"_blank\" rel=\"noopener\">koefisien korelasi Pearson<\/a> , yang mengukur hubungan linear antara dua variabel <em>.<\/em><\/span><\/p>\n<p> <span style=\"color: #000000;\">Itu selalu mengambil nilai antara -1 dan 1 di mana:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">-1 menunjukkan korelasi linier negatif sempurna antara dua variabel<\/span><\/li>\n<li> <span style=\"color: #000000;\">0 menunjukkan tidak ada korelasi linier antara dua variabel<\/span><\/li>\n<li> <span style=\"color: #000000;\">Angka 1 menunjukkan korelasi linier positif sempurna antara dua variabel<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Semakin jauh koefisien korelasi dari nol maka semakin kuat hubungan kedua variabel tersebut.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Contoh berikut menunjukkan cara menggunakan <strong>proc corr<\/strong> di SAS untuk menghitung koefisien korelasi antar variabel dalam kumpulan data terintegrasi SAS yang disebut <a href=\"https:\/\/documentation.sas.com\/doc\/en\/pgmsascdc\/9.4_3.4\/statug\/statug_sashelp_sect012.htm\" target=\"_blank\" rel=\"noopener\">Fish<\/a> , yang berisi berbagai pengukuran untuk 159 ikan berbeda yang ditangkap di sebuah danau di Finlandia.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Kita dapat menggunakan <strong>proc print<\/strong> untuk menampilkan 10 observasi pertama dari kumpulan data ini:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">\/*view first 10 observations from <em>Fish<\/em> dataset*\/\n<\/span><span style=\"color: #800080;\">proc print<\/span> <span style=\"color: #3366ff;\">data<\/span> =sashelp.Fish( <span style=\"color: #3366ff;\">obs<\/span> = <span style=\"color: #008000;\">10<\/span> );\n\n<span style=\"color: #800080;\">run<\/span> ;\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-22476 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/poisson1.jpg\" alt=\"\" width=\"492\" height=\"281\" srcset=\"\" sizes=\"\"><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Contoh 1: Korelasi antara dua variabel<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Kita dapat menggunakan kode berikut untuk menghitung koefisien korelasi Pearson antara variabel Tinggi dan Lebar:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">\/*calculate correlation coefficient between Height and Width*\/\n<\/span><span style=\"color: #800080;\"><span style=\"color: #000000;\"><span style=\"color: #800080;\">proc corr<\/span> <span style=\"color: #3366ff;\">data<\/span> =sashelp.fish;\n\t<span style=\"color: #3366ff;\">var<\/span> HeightWidth;\n<\/span>\n<span style=\"color: #800080;\">run<\/span> ;\n<\/span><\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-22491 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/corrsas1.jpg\" alt=\"\" width=\"434\" height=\"333\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">Tabel pertama menampilkan statistik ringkasan untuk tinggi dan lebar.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Tabel kedua menampilkan koefisien korelasi Pearson antara kedua variabel, termasuk <a href=\"https:\/\/statorials.org\/id\/p-menghargai-signifikansi-statistik\/\" target=\"_blank\" rel=\"noopener\">nilai p<\/a> yang menunjukkan apakah korelasi tersebut signifikan secara statistik.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Dari hasilnya kita dapat melihat:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Koefisien korelasi Pearson: <strong>0,79288<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">Nilai P: <strong>&lt;0,0001<\/strong><\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Hal ini menunjukkan bahwa terdapat korelasi positif yang kuat antara tinggi dan lebar dan korelasi tersebut signifikan secara statistik karena nilai p kurang dari \u03b1 = 0,05.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Terkait:<\/strong> <a href=\"https:\/\/statorials.org\/id\" target=\"_blank\" rel=\"noopener\">Apa yang dianggap sebagai korelasi \u201ckuat\u201d?<\/a><\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Contoh 2: Korelasi antar semua variabel<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Kita dapat menggunakan kode berikut untuk menghitung koefisien korelasi Pearson antara semua kombinasi variabel berpasangan dalam kumpulan data:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">\/*calculate correlation coefficient between all pairwise combinations of variables*\/\n<\/span><span style=\"color: #800080;\"><span style=\"color: #000000;\"><span style=\"color: #800080;\">proc corr<\/span> <span style=\"color: #3366ff;\">data<\/span> =sashelp.fish;\n<\/span>\nrun;\n<\/span><\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-22492\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/corrsas2.jpg\" alt=\"matriks korelasi di SAS\" width=\"447\" height=\"649\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">Hasilnya menampilkan <a href=\"https:\/\/statorials.org\/id\/cara-membaca-matriks-korelasi\/\" target=\"_blank\" rel=\"noopener\">matriks korelasi<\/a> , yang berisi koefisien korelasi Pearson dan nilai p yang sesuai untuk setiap kombinasi berpasangan variabel numerik dalam kumpulan data.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Misalnya:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Koefisien korelasi Pearson antara berat dan panjang1 adalah <strong>0,91644.<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">Koefisien korelasi Pearson antara berat dan panjang2 adalah <strong>0,91937.<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">Koefisien korelasi Pearson antara berat dan panjang3 adalah <strong>0,92447.<\/strong><\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Dan seterusnya.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Contoh 3: Visualisasikan korelasinya dengan plot sebar<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Kita juga dapat menggunakan fungsi <strong>plots<\/strong> untuk membuat plot sebar guna memvisualisasikan korelasi antara dua variabel:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">\/*visualize correlation between Height and Width*\/\n<\/span><span style=\"color: #800080;\"><span style=\"color: #000000;\"><span style=\"color: #800080;\">proc corr<\/span> <span style=\"color: #3366ff;\">data<\/span> =sashelp.fish <span style=\"color: #3366ff;\">plots<\/span> =scatter( <span style=\"color: #3366ff;\">nvar<\/span> =all);;\n\t<span style=\"color: #3366ff;\">var<\/span> HeightWidth;\n<\/span>\nrun;<\/span><\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-22493 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/corrsas3.jpg\" alt=\"\" width=\"569\" height=\"448\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">Pada grafik kita dapat melihat korelasi positif yang kuat antara tinggi dan lebar. Semakin bertambah tinggi maka lebarnya pun cenderung bertambah.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Di pojok kiri atas grafik kita juga dapat melihat total observasi yang digunakan, koefisien korelasi dan nilai p dari koefisien korelasi.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Sumber daya tambahan<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Tutorial berikut menjelaskan cara melakukan operasi umum lainnya di SAS:<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/id\/tabel-frekuensi-sas\/\" target=\"_blank\" rel=\"noopener\">Cara membuat tabel frekuensi di SAS<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/prosedur-ringkasan-di-sas\/\" target=\"_blank\" rel=\"noopener\">Cara menghitung statistik deskriptif di SAS<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Salah satu cara untuk mengukur hubungan antara dua variabel adalah dengan menggunakan koefisien korelasi Pearson , yang mengukur hubungan linear antara dua variabel . Itu selalu mengambil nilai antara -1 dan 1 di mana: -1 menunjukkan korelasi linier negatif sempurna antara dua variabel 0 menunjukkan tidak ada korelasi linier antara dua variabel Angka 1 menunjukkan [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Cara Menghitung Korelasi di SAS (dengan Contoh) - Statorial<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara menghitung korelasi di SAS, dengan beberapa contoh.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/id\/korelasi-di-airlock\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cara Menghitung Korelasi di SAS (dengan Contoh) - Statorial\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara menghitung korelasi di SAS, dengan beberapa contoh.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/korelasi-di-airlock\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-21T21:07:56+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/poisson1.jpg\" \/>\n<meta name=\"author\" content=\"Benjamin anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Ditulis oleh\" \/>\n\t<meta name=\"twitter:data1\" content=\"Benjamin anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Estimasi waktu membaca\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 menit\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/id\/korelasi-di-airlock\/\",\"url\":\"https:\/\/statorials.org\/id\/korelasi-di-airlock\/\",\"name\":\"Cara Menghitung Korelasi di SAS (dengan Contoh) - Statorial\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-21T21:07:56+00:00\",\"dateModified\":\"2023-07-21T21:07:56+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara menghitung korelasi di SAS, dengan beberapa contoh.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/korelasi-di-airlock\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/korelasi-di-airlock\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/korelasi-di-airlock\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara menghitung korelasi di sas (dengan contoh)\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/id\/#website\",\"url\":\"https:\/\/statorials.org\/id\/\",\"name\":\"Statorials\",\"description\":\"Panduan anda untuk kompetensi statistik!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/id\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"id\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\",\"name\":\"Benjamin anderson\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"id\",\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/image\/\",\"url\":\"http:\/\/statorials.org\/id\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"http:\/\/statorials.org\/id\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"caption\":\"Benjamin anderson\"},\"description\":\"Halo, saya Benjamin, pensiunan profesor statistika yang menjadi guru Statorial yang berdedikasi. 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