{"id":1312,"date":"2023-07-26T22:13:41","date_gmt":"2023-07-26T22:13:41","guid":{"rendered":"https:\/\/statorials.org\/id\/residu-python-standar\/"},"modified":"2023-07-26T22:13:41","modified_gmt":"2023-07-26T22:13:41","slug":"residu-python-standar","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/residu-python-standar\/","title":{"rendered":"Cara menghitung residu standar dengan python"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>Residual<\/strong> adalah selisih antara nilai observasi dan nilai prediksi dalam <a href=\"https:\/\/statorials.org\/id\/regresi-linier-1\/\" target=\"_blank\" rel=\"noopener noreferrer\">model regresi<\/a> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ini dihitung sebagai berikut:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Sisa = Nilai yang diamati \u2013 Nilai yang diprediksi<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Jika kita memplot nilai observasi dan menempatkan garis regresi yang dipasang, residu untuk setiap <a href=\"https:\/\/statorials.org\/id\/pengamatan-dalam-statistik\/\" target=\"_blank\" rel=\"noopener\">observasi<\/a> akan menjadi jarak vertikal antara observasi dan garis regresi:<\/span> <\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-12422 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/residus1-1.png\" alt=\"Contoh residu dalam statistik\" width=\"487\" height=\"382\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">Salah satu jenis residu yang sering kita gunakan untuk mengidentifikasi outlier dalam model regresi disebut <strong>residu terstandarisasi<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ini dihitung sebagai berikut:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>r <sub>i<\/sub> = e <sub>i<\/sub> \/ s( <sub>ei<\/sub> )<\/strong> = <strong>e <sub>i<\/sub> \/ RSE\u221a <span style=\"border-top: 1px solid black;\">1-h <sub>ii<\/sub><\/span><\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Emas:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>e <sub>i<\/sub> :<\/strong> Residu <sup>ke<\/sup> -i<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>RSE:<\/strong> kesalahan standar sisa model<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>h <sub>ii<\/sub><\/strong> : Meningkatnya observasi <sup>ke-i<\/sup><\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Dalam praktiknya, kita sering menganggap residu terstandar yang nilai absolutnya lebih besar dari 3 sebagai outlier.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Tutorial ini memberikan contoh langkah demi langkah tentang cara menghitung residu standar dengan Python.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Langkah 1: Masukkan datanya<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Pertama, kita akan membuat kumpulan data kecil untuk digunakan dengan Python:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#create dataset\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #008000;\">x<\/span> ': [8, 12, 12, 13, 14, 16, 17, 22, 24, 26, 29, 30],\n                   ' <span style=\"color: #008000;\">y<\/span> ': [41, 42, 39, 37, 35, 39, 45, 46, 39, 49, 55, 57]})\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Langkah 2: Sesuaikan model regresi<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Selanjutnya, kita akan memasang <a href=\"https:\/\/statorials.org\/id\/regresi-linier-sederhana-dengan-python\/\" target=\"_blank\" rel=\"noopener\">model regresi linier sederhana<\/a> :<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> statsmodels. <span style=\"color: #3366ff;\">api<\/span> <span style=\"color: #008000;\">as<\/span> sm\n\n<span style=\"color: #008080;\">#define response variable\n<\/span>y = df[' <span style=\"color: #008000;\">y<\/span> ']\n\n<span style=\"color: #008080;\">#define explanatory variable\n<\/span>x = df[' <span style=\"color: #008000;\">x<\/span> ']\n\n<span style=\"color: #008080;\">#add constant to predictor variables\n<\/span>x = sm. <span style=\"color: #3366ff;\">add_constant<\/span> (x)\n\n<span style=\"color: #008080;\">#fit linear regression model\n<\/span>model = sm. <span style=\"color: #3366ff;\">OLS<\/span> (y,x). <span style=\"color: #3366ff;\">fit<\/span> ()<\/strong><\/span><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Langkah 3: Hitung residu standar<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Selanjutnya, kami akan menghitung residu standar model:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create instance of influence\n<\/span>influence = model. <span style=\"color: #3366ff;\">get_influence<\/span> ()\n\n<span style=\"color: #008080;\">#obtain standardized residuals\n<\/span>standardized_residuals = influence. <span style=\"color: #3366ff;\">reside_studentized_internal<\/span>\n\n<span style=\"color: #008080;\">#display standardized residuals\n<\/span><span style=\"color: #993300;\">print<\/span> (standardized_residuals)\n\n[ 1.40517322 0.81017562 0.07491009 -0.59323342 -1.2482053 -0.64248883\n  0.59610905 -0.05876884 -2.11711982 -0.066556 0.91057211 1.26973888]<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Dari hasil tersebut, kita dapat melihat bahwa tidak ada satu pun residu terstandar yang melebihi nilai absolut 3. Dengan demikian, tidak ada satu pun observasi yang tampak outlier.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Langkah 4: Visualisasikan residu standar<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Terakhir, kita dapat membuat plot sebar untuk memvisualisasikan nilai variabel prediktor terhadap residu standar:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n\nplt. <span style=\"color: #3366ff;\">scatter<\/span> (df.x, standardized_residuals)\nplt. <span style=\"color: #3366ff;\">xlabel<\/span> (' <span style=\"color: #008000;\">x<\/span> ')\nplt. <span style=\"color: #3366ff;\">ylabel<\/span> (' <span style=\"color: #008000;\">Standardized Residuals<\/span> ')\nplt. <span style=\"color: #3366ff;\">axhline<\/span> (y=0, color=' <span style=\"color: #008000;\">black<\/span> ', linestyle=' <span style=\"color: #008000;\">--<\/span> ', linewidth=1)\nplt. <span style=\"color: #3366ff;\">show<\/span> ()<\/span><\/span><\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Sumber daya tambahan<\/strong><\/span><\/h3>\n<p> <a href=\"https:\/\/statorials.org\/id\/residu\/\" target=\"_blank\" rel=\"noopener\">Apa itu residu?<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/residu-terstandar\/\">Apa yang dimaksud dengan residu terstandar?<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/residu-distandarisasi-di-r\/\" target=\"_blank\" rel=\"noopener\">Cara menghitung residu standar di R<\/a><br \/><a href=\"https:\/\/statorials.org\/id\/residu-yang-dinormalisasi-excel\/\" target=\"_blank\" rel=\"noopener\">Cara menghitung residu standar di Excel<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Residual adalah selisih antara nilai observasi dan nilai prediksi dalam model regresi . Ini dihitung sebagai berikut: Sisa = Nilai yang diamati \u2013 Nilai yang diprediksi Jika kita memplot nilai observasi dan menempatkan garis regresi yang dipasang, residu untuk setiap observasi akan menjadi jarak vertikal antara observasi dan garis regresi: Salah satu jenis residu yang [&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 Residu Standar dengan Python<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara menghitung residu standar dengan Python, dengan sebuah 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\/residu-python-standar\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cara Menghitung Residu Standar dengan Python\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara menghitung residu standar dengan Python, dengan sebuah contoh.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/residu-python-standar\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-26T22:13:41+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/residus1-1.png\" \/>\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\/residu-python-standar\/\",\"url\":\"https:\/\/statorials.org\/id\/residu-python-standar\/\",\"name\":\"Cara Menghitung Residu Standar dengan Python\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-26T22:13:41+00:00\",\"dateModified\":\"2023-07-26T22:13:41+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara menghitung residu standar dengan Python, dengan sebuah contoh.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/residu-python-standar\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/residu-python-standar\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/residu-python-standar\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara menghitung residu standar dengan python\"}]},{\"@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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