{"id":1338,"date":"2023-07-26T19:53:42","date_gmt":"2023-07-26T19:53:42","guid":{"rendered":"https:\/\/statorials.org\/id\/jumlah-sisa-kotak-dengan-python\/"},"modified":"2023-07-26T19:53:42","modified_gmt":"2023-07-26T19:53:42","slug":"jumlah-sisa-kotak-dengan-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/jumlah-sisa-kotak-dengan-python\/","title":{"rendered":"Cara menghitung jumlah sisa kuadrat dengan python"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/id\/residu\/\" target=\"_blank\" rel=\"noopener\">Residual<\/a> adalah selisih antara nilai observasi dan nilai prediksi dalam model regresi.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ini dihitung sebagai berikut:<\/span><\/p>\n<p> <span style=\"color: #000000;\">Sisa = Nilai yang diamati \u2013 Nilai yang diprediksi<\/span><\/p>\n<p> <span style=\"color: #000000;\">Salah satu cara untuk memahami seberapa cocok model regresi dengan kumpulan data adalah dengan menghitung <strong>jumlah sisa kuadrat<\/strong> , yang dihitung sebagai berikut:<\/span><\/p>\n<p> <span style=\"color: #000000;\">Jumlah sisa kuadrat = \u03a3( <sub>ei<\/sub> ) <sup>2<\/sup><\/span><\/p>\n<p> <span style=\"color: #000000;\">Emas:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\u03a3<\/strong> : Simbol Yunani yang berarti \u201cjumlah\u201d<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>e <sub>i<\/sub><\/strong> : Residu <sup>ke<\/sup> -i<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Semakin rendah nilainya, semakin baik model tersebut cocok dengan kumpulan data.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Tutorial ini memberikan contoh langkah demi langkah tentang cara menghitung jumlah sisa kuadrat untuk model regresi dengan Python.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Langkah 1: Masukkan datanya<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Untuk contoh ini, kami akan memasukkan data terkait jumlah jam belajar, jumlah total ujian persiapan yang diambil, dan hasil ujian yang diperoleh 14 siswa berbeda:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> pandas <span style=\"color: #107d3f;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame<\/span>\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #008000;\">hours<\/span> ': [1, 2, 2, 4, 2, 1, 5, 4, 2, 4, 4, 3, 6, 5],\n                   ' <span style=\"color: #008000;\">exams<\/span> ': [1, 3, 3, 5, 2, 2, 1, 1, 0, 3, 4, 3, 2, 4],\n                   ' <span style=\"color: #008000;\">score<\/span> ': [76, 78, 85, 88, 72, 69, 94, 94, 88, 92, 90, 75, 96, 90]})\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 menggunakan<\/span> <a href=\"https:\/\/www.statsmodels.org\/devel\/generated\/statsmodels.regression.linear_model.OLS.html\" target=\"_blank\" rel=\"noopener noreferrer\">fungsi OLS()<\/a> <span style=\"color: #000000;\">dari pustaka statsmodels untuk melakukan regresi kuadrat terkecil biasa, menggunakan &#8220;jam&#8221; dan &#8220;ujian&#8221; sebagai variabel prediktor dan &#8220;skor&#8221; sebagai variabel respons:<\/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;\">score<\/span> ']\n\n<span style=\"color: #008080;\">#define predictor variables\n<\/span>x = df[[' <span style=\"color: #008000;\">hours<\/span> ', ' <span style=\"color: #008000;\">exams<\/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> ()\n\n<span style=\"color: #008080;\">#view model summary\n<\/span><span style=\"color: #993300;\">print<\/span> ( <span style=\"color: #3366ff;\">model.summary<\/span> ())\n\n                            OLS Regression Results                            \n==================================================== ============================\nDept. Variable: R-squared score: 0.722\nModel: OLS Adj. R-squared: 0.671\nMethod: Least Squares F-statistic: 14.27\nDate: Sat, 02 Jan 2021 Prob (F-statistic): 0.000878\nTime: 15:58:35 Log-Likelihood: -41.159\nNo. Comments: 14 AIC: 88.32\nDf Residuals: 11 BIC: 90.24\nModel: 2                                         \nCovariance Type: non-robust                                         \n==================================================== ============================\n                 coef std err t P&gt;|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 71.8144 3.680 19.517 0.000 63.716 79.913\nhours 5.0318 0.942 5.339 0.000 2.958 7.106\nexams -1.3186 1.063 -1.240 0.241 -3.658 1.021\n==================================================== ============================\nOmnibus: 0.976 Durbin-Watson: 1.270\nProb(Omnibus): 0.614 Jarque-Bera (JB): 0.757\nSkew: -0.245 Prob(JB): 0.685\nKurtosis: 1.971 Cond. No. 12.1\n==================================================== ============================\n<\/strong><\/span><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Langkah 3: Hitung jumlah sisa kuadrat<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Kita dapat menggunakan kode berikut untuk menghitung jumlah sisa kuadrat model:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #993300;\">print<\/span> ( <span style=\"color: #3366ff;\">model.ssr<\/span> )\n\n293.25612951525414\n<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">Jumlah sisa kuadratnya adalah <strong>293.256<\/strong> .<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Sumber daya tambahan<\/strong><\/span><\/h3>\n<p> <a href=\"https:\/\/statorials.org\/id\/regresi-linier-sederhana-dengan-python\/\" target=\"_blank\" rel=\"noopener\">Cara melakukan regresi linier sederhana dengan Python<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/python-regresi-linier\/\" target=\"_blank\" rel=\"noopener\">Cara melakukan regresi linier berganda dengan Python<\/a><br \/> Kalkulator Jumlah Sisa Kuadrat<\/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 Salah satu cara untuk memahami seberapa cocok model regresi dengan kumpulan data adalah dengan menghitung jumlah sisa kuadrat , yang dihitung sebagai berikut: Jumlah sisa kuadrat = \u03a3( ei ) [&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 jumlah sisa kuadrat dengan Python - Statorials<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara menghitung jumlah sisa kuadrat untuk model regresi 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\/jumlah-sisa-kotak-dengan-python\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cara menghitung jumlah sisa kuadrat dengan Python - Statorials\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara menghitung jumlah sisa kuadrat untuk model regresi dengan Python, dengan sebuah contoh.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/jumlah-sisa-kotak-dengan-python\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-26T19:53:42+00:00\" \/>\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\/jumlah-sisa-kotak-dengan-python\/\",\"url\":\"https:\/\/statorials.org\/id\/jumlah-sisa-kotak-dengan-python\/\",\"name\":\"Cara menghitung jumlah sisa kuadrat dengan Python - Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-26T19:53:42+00:00\",\"dateModified\":\"2023-07-26T19:53:42+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara menghitung jumlah sisa kuadrat untuk model regresi dengan Python, dengan sebuah contoh.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/jumlah-sisa-kotak-dengan-python\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/jumlah-sisa-kotak-dengan-python\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/jumlah-sisa-kotak-dengan-python\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara menghitung jumlah sisa kuadrat 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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