{"id":1063,"date":"2023-07-27T19:30:41","date_gmt":"2023-07-27T19:30:41","guid":{"rendered":"https:\/\/statorials.org\/id\/kesalahan-standar-sisa-r\/"},"modified":"2023-07-27T19:30:41","modified_gmt":"2023-07-27T19:30:41","slug":"kesalahan-standar-sisa-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/kesalahan-standar-sisa-r\/","title":{"rendered":"Cara menghitung sisa kesalahan standar di r"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">Setiap kali kita memasukkan model regresi linier ke dalam R, model tersebut mengambil bentuk berikut:<\/span><\/p>\n<p> <span style=\"color: #000000;\">Y = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub> X + \u2026 + \u03b2 <sub>saya<\/sub><\/span><\/p>\n<p> <span style=\"color: #000000;\">dimana \u03f5 adalah suku kesalahan yang tidak bergantung pada X.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Tidak peduli bagaimana X dapat digunakan untuk memprediksi nilai Y, akan selalu ada kesalahan acak dalam model. Salah satu cara untuk mengukur sebaran kesalahan acak ini adalah dengan menggunakan <strong>kesalahan standar sisa<\/strong> , yaitu cara mengukur simpangan baku dari sisa \u03f5.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Kesalahan standar sisa model regresi dihitung sebagai berikut:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Kesalahan standar sisa = \u221a <span style=\"border-top: 1px solid black;\"><sub>Residu<\/sub> SS \/ <sub>residu<\/sub> df<\/span><\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Emas:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong><sub>Residual<\/sub> SS<\/strong> : Jumlah sisa kuadrat.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong><sub>sisa<\/sub> df<\/strong> : sisa derajat kebebasan, dihitung sebagai n \u2013 k \u2013 1 dimana n = jumlah observasi dan k = jumlah parameter model.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Ada tiga metode yang dapat kita gunakan untuk menghitung sisa standar error model regresi di R.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Metode 1: Analisis ringkasan model<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Cara pertama untuk mendapatkan sisa standar error adalah dengan menyesuaikan model regresi linier dan kemudian menggunakan perintah <strong>ringkasan()<\/strong> untuk mendapatkan hasil model. Kemudian cari saja &#8220;kesalahan standar sisa&#8221; di bagian bawah keluaran:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load built-in <em>mtcars<\/em> dataset<\/span>\ndata(mtcars)\n\n<span style=\"color: #008080;\">#fit regression model\n<\/span>model &lt;- lm(mpg~disp+hp, data=mtcars)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model)\n\nCall:\nlm(formula = mpg ~ disp + hp, data = mtcars)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-4.7945 -2.3036 -0.8246 1.8582 6.9363 \n\nCoefficients:\n             Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) 30.735904 1.331566 23.083 &lt; 2nd-16 ***\navailable -0.030346 0.007405 -4.098 0.000306 ***\nhp -0.024840 0.013385 -1.856 0.073679 .  \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: <span style=\"color: #008000;\">3.127<\/span> on 29 degrees of freedom\nMultiple R-squared: 0.7482, Adjusted R-squared: 0.7309 \nF-statistic: 43.09 on 2 and 29 DF, p-value: 2.062e-09<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Kita dapat melihat bahwa kesalahan standar sisa adalah <strong>3.127<\/strong> .<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Metode 2: Gunakan Rumus Sederhana<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Cara lain untuk mendapatkan sisa standar error (RSE) adalah dengan menyesuaikan model regresi linier dan kemudian menggunakan rumus berikut untuk menghitung RSE:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>sqrt( <span style=\"color: #3366ff;\">deviance<\/span> (model)\/df. <span style=\"color: #3366ff;\">residual<\/span> (model))\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Berikut cara menerapkan rumus ini di R:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load built-in <em>mtcars<\/em> dataset<\/span>\ndata(mtcars)\n\n<span style=\"color: #008080;\">#fit regression model\n<\/span>model &lt;- lm(mpg~disp+hp, data=mtcars)\n\n<span style=\"color: #008080;\">#calculate residual standard error\n<\/span>sqrt( <span style=\"color: #3366ff;\">deviance<\/span> (model)\/df. <span style=\"color: #3366ff;\">residual<\/span> (model))\n\n[1] 3.126601\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Kita dapat melihat bahwa kesalahan standar sisa adalah <strong>3.126601<\/strong> .<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Metode 3: Gunakan rumus langkah demi langkah<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Cara lain untuk mendapatkan kesalahan standar sisa adalah dengan menyesuaikan model regresi linier dan kemudian menggunakan pendekatan langkah demi langkah untuk menghitung setiap komponen rumus RSE:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load built-in <em>mtcars<\/em> dataset<\/span>\ndata(mtcars)\n\n<span style=\"color: #008080;\">#fit regression model\n<\/span>model &lt;- lm(mpg~disp+hp, data=mtcars)\n\n<span style=\"color: #008080;\">#calculate the number of model parameters - 1\n<\/span>k=length(model$ <span style=\"color: #3366ff;\">coefficients<\/span> )-1\n\n<span style=\"color: #008080;\">#calculate sum of squared residuals<\/span>\nSSE=sum(model$ <span style=\"color: #3366ff;\">residuals<\/span> **2)\n\n<span style=\"color: #008080;\">#calculate total observations in dataset<\/span>\nn=length(model$ <span style=\"color: #3366ff;\">residuals<\/span> )\n\n<span style=\"color: #008080;\">#calculate residual standard error<\/span>\nsqrt(SSE\/(n-(1+k)))\n\n[1] 3.126601\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Kita dapat melihat bahwa kesalahan standar sisa adalah <strong>3.126601<\/strong> .<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Bagaimana menafsirkan kesalahan standar sisa<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Seperti disebutkan sebelumnya, sisa standar error (RSE) adalah cara untuk mengukur simpangan baku dari sisa dalam model regresi.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Semakin rendah nilai CSR, semakin baik model tersebut mampu menyesuaikan dengan data (namun hati-hati jangan sampai <a href=\"https:\/\/statorials.org\/id\/pembelajaran-mesin-yang-berlebihan\/\" target=\"_blank\" rel=\"noopener noreferrer\">overfitting<\/a> ). Ini bisa menjadi metrik yang berguna untuk digunakan saat membandingkan dua model atau lebih guna menentukan model mana yang paling sesuai dengan data.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Sumber daya tambahan<\/strong><\/span><\/h3>\n<p> <a href=\"https:\/\/statorials.org\/id\/bagaimana-menafsirkan-kesalahan-standar-sisa\/\">Bagaimana menafsirkan kesalahan standar sisa<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/regresi-linier-berganda-r\/\" target=\"_blank\" rel=\"noopener noreferrer\">Cara melakukan regresi linier berganda di R<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/cara-melakukan-validasi-silang-untuk-kinerja-model-di-r\/\" target=\"_blank\" rel=\"noopener noreferrer\">Cara melakukan validasi silang untuk kinerja model di R<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/simpangan-baku-di-r\/\" target=\"_blank\" rel=\"noopener noreferrer\">Cara menghitung simpangan baku di R<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Setiap kali kita memasukkan model regresi linier ke dalam R, model tersebut mengambil bentuk berikut: Y = \u03b2 0 + \u03b2 1 X + \u2026 + \u03b2 saya dimana \u03f5 adalah suku kesalahan yang tidak bergantung pada X. Tidak peduli bagaimana X dapat digunakan untuk memprediksi nilai Y, akan selalu ada kesalahan acak dalam model. [&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 sisa kesalahan standar di R - Statorials<\/title>\n<meta name=\"description\" content=\"Penjelasan sederhana tentang cara menghitung sisa standar error model regresi di R, termasuk contohnya.\" \/>\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\/kesalahan-standar-sisa-r\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cara menghitung sisa kesalahan standar di R - Statorials\" \/>\n<meta property=\"og:description\" content=\"Penjelasan sederhana tentang cara menghitung sisa standar error model regresi di R, termasuk contohnya.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/kesalahan-standar-sisa-r\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-27T19:30:41+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=\"3 menit\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/id\/kesalahan-standar-sisa-r\/\",\"url\":\"https:\/\/statorials.org\/id\/kesalahan-standar-sisa-r\/\",\"name\":\"Cara menghitung sisa kesalahan standar di R - Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-27T19:30:41+00:00\",\"dateModified\":\"2023-07-27T19:30:41+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Penjelasan sederhana tentang cara menghitung sisa standar error model regresi di R, termasuk contohnya.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/kesalahan-standar-sisa-r\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/kesalahan-standar-sisa-r\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/kesalahan-standar-sisa-r\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara menghitung sisa kesalahan standar di r\"}]},{\"@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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