{"id":951,"date":"2023-07-28T05:19:18","date_gmt":"2023-07-28T05:19:18","guid":{"rendered":"https:\/\/statorials.org\/id\/uji-grubbs-python\/"},"modified":"2023-07-28T05:19:18","modified_gmt":"2023-07-28T05:19:18","slug":"uji-grubbs-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/uji-grubbs-python\/","title":{"rendered":"Cara menjalankan penguji grubbs dengan python"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>Tes Grubbs<\/strong> digunakan untuk mengidentifikasi keberadaan outlier dalam suatu kumpulan data. Untuk menggunakan tes ini, kumpulan data harus terdistribusi normal dan berisi setidaknya 7 observasi.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Tutorial ini menjelaskan cara melakukan tes Grubbs dengan Python.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Tes Grubbs dengan Python<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Untuk melakukan tes Grubbs dengan Python, kita dapat menggunakan fungsi smirnov_grubbs() dari paket <a href=\"https:\/\/pypi.org\/project\/outlier_utils\/\" target=\"_blank\" rel=\"noopener noreferrer\">outlier_utils<\/a> , yang menggunakan sintaks berikut:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>smirnov_grubbs.test (data, alfa = 0,05)<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Emas:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>data:<\/strong> vektor numerik dari nilai data<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>alpha:<\/strong> Tingkat signifikansi yang digunakan untuk tes. Nilai defaultnya adalah 0,05<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Untuk menggunakan fitur ini, Anda harus menginstal paket <a href=\"https:\/\/pypi.org\/project\/outlier_utils\/\" target=\"_blank\" rel=\"noopener noreferrer\">outlier_utils<\/a> terlebih dahulu:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>pip install outlier_utils\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Setelah paket ini terinstal, Anda dapat melakukan tes Grubbs. Contoh berikut mengilustrasikan cara melakukan hal ini.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Contoh 1: Tes Grubbs dua sisi<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Kode berikut mengilustrasikan cara melakukan pengujian Grubbs dua sisi, yang akan mendeteksi outlier di kedua ujung kumpulan data.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> numpy <span style=\"color: #107d3f;\">as<\/span> np\n<span style=\"color: #008000;\">from<\/span> outliers <span style=\"color: #008000;\">import<\/span> smirnov_grubbs <span style=\"color: #008000;\">as<\/span> grubbs\n\n<span style=\"color: #008080;\">#define data<\/span>\ndata = np.array([5, 14, 15, 15, 14, 19, 17, 16, 20, 22, 8, 21, 28, 11, 9, 29, 40])\n\n<span style=\"color: #008080;\">#perform Grubbs' test<\/span>\ngrubbs. <span style=\"color: #3366ff;\">test<\/span> (data, alpha=.05)\n\narray([5, 14, 15, 15, 14, 19, 17, 16, 20, 22, 8, 21, 28, 11, 9, 29])\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Fungsi ini hanya mengembalikan array tanpa outlier. Dalam kasus ini, nilai maksimum 40 merupakan outlier dan oleh karena itu dihilangkan.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Contoh 2: Tes Grubbs satu sisi<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara melakukan tes Grubbs satu sisi untuk nilai minimum dan nilai maksimum dalam kumpulan data:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> numpy <span style=\"color: #107d3f;\">as<\/span> np\n<span style=\"color: #008000;\">from<\/span> outliers <span style=\"color: #008000;\">import<\/span> smirnov_grubbs <span style=\"color: #008000;\">as<\/span> grubbs\n\n<span style=\"color: #008080;\">#define data<\/span>\ndata = np.array([5, 14, 15, 15, 14, 19, 17, 16, 20, 22, 8, 21, 28, 11, 9, 29, 40])\n\n<span style=\"color: #008080;\">#perform Grubbs' test to see if minimum value is an outlier<\/span>\ngrubbs. <span style=\"color: #3366ff;\">min_test<\/span> (data, alpha=.05)\n\narray([5, 14, 15, 15, 14, 19, 17, 16, 20, 22, 8, 21, 28, 11, 9, 29, 40])\n\n<span style=\"color: #008080;\">#perform Grubbs' test to see if minimum value is an outlier\n<\/span>grubbs. <span style=\"color: #3366ff;\">max_test<\/span> (data, alpha=.05)\n\narray([5, 14, 15, 15, 14, 19, 17, 16, 20, 22, 8, 21, 28, 11, 9, 29])\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Uji minimum outlier tidak mendeteksi nilai minimum sebagai outlier. Namun, uji outlier maksimum menentukan bahwa nilai maksimum 40 adalah outlier dan oleh karena itu dihilangkan.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Contoh 3: Ekstrak indeks outlier<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara mengekstrak indeks outlier:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> numpy <span style=\"color: #107d3f;\">as<\/span> np\n<span style=\"color: #008000;\">from<\/span> outliers <span style=\"color: #008000;\">import<\/span> smirnov_grubbs <span style=\"color: #008000;\">as<\/span> grubbs\n\n<span style=\"color: #008080;\">#define data<\/span>\ndata = np.array([5, 14, 15, 15, 14, 19, 17, 16, 20, 22, 8, 21, 28, 11, 9, 29, 40])\n\n<span style=\"color: #008080;\">#perform Grubbs' test and identify index (if any) of the outlier<\/span>\ngrubbs. <span style=\"color: #3366ff;\">max_test_indices<\/span> (data, alpha=.05)\n\n[16]\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Hal ini menunjukkan bahwa terdapat outlier pada posisi indeks 16 pada tabel.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Contoh 4: Ekstrak nilai dari outlier<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara mengekstrak nilai dari outlier:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> numpy <span style=\"color: #107d3f;\">as<\/span> np\n<span style=\"color: #008000;\">from<\/span> outliers <span style=\"color: #008000;\">import<\/span> smirnov_grubbs <span style=\"color: #008000;\">as<\/span> grubbs\n\n<span style=\"color: #008080;\">#define data<\/span>\ndata = np.array([5, 14, 15, 15, 14, 19, 17, 16, 20, 22, 8, 21, 28, 11, 9, 29, 40])\n\n<span style=\"color: #008080;\">#perform Grubbs' test and identify the actual value (if any) of the outlier<\/span>\ngrubbs. <span style=\"color: #3366ff;\">max_test_outliers<\/span> (data, alpha=.05)\n\n[40]\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Ini memberitahu kita bahwa ada outlier dengan nilai 40.<\/span><\/p>\n<h3> <strong>Cara menangani outlier<\/strong><\/h3>\n<p> <span style=\"color: #000000;\">Jika pengujian Grubbs mengidentifikasi outlier dalam kumpulan data Anda, Anda memiliki beberapa opsi:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. Periksa kembali apakah nilainya bukan kesalahan ketik atau kesalahan entri data.<\/strong> Terkadang nilai yang muncul sebagai outlier dalam kumpulan data hanyalah kesalahan ketik yang dilakukan oleh seseorang selama entri data. Pertama, verifikasi bahwa nilai telah dimasukkan dengan benar sebelum mengambil keputusan lebih lanjut.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2. Tetapkan nilai baru pada outlier<\/strong> . Jika outlier ternyata disebabkan oleh kesalahan ketik atau kesalahan entri data, Anda dapat memutuskan untuk memberinya nilai baru, misalnya mean<\/span> <a href=\"https:\/\/statorials.org\/id\/mengukur-tendensi-sentral\/\" target=\"_blank\" rel=\"noopener noreferrer\">atau median<\/a> <span style=\"color: #000000;\">kumpulan data.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>3. Hapus outlier.<\/strong> Jika nilainya benar-benar outlier, Anda dapat memilih untuk menghapusnya jika nilai tersebut akan berdampak signifikan pada analisis Anda.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tes Grubbs digunakan untuk mengidentifikasi keberadaan outlier dalam suatu kumpulan data. Untuk menggunakan tes ini, kumpulan data harus terdistribusi normal dan berisi setidaknya 7 observasi. Tutorial ini menjelaskan cara melakukan tes Grubbs dengan Python. Tes Grubbs dengan Python Untuk melakukan tes Grubbs dengan Python, kita dapat menggunakan fungsi smirnov_grubbs() dari paket outlier_utils , yang menggunakan [&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 Melakukan Tes Grubbs dengan Python - Statologi<\/title>\n<meta name=\"description\" content=\"Penjelasan sederhana tentang cara melakukan tes Grubbs dengan Python untuk mendeteksi outlier.\" \/>\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\/uji-grubbs-python\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cara Melakukan Tes Grubbs dengan Python - Statologi\" \/>\n<meta property=\"og:description\" content=\"Penjelasan sederhana tentang cara melakukan tes Grubbs dengan Python untuk mendeteksi outlier.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/uji-grubbs-python\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-28T05:19:18+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\/uji-grubbs-python\/\",\"url\":\"https:\/\/statorials.org\/id\/uji-grubbs-python\/\",\"name\":\"Cara Melakukan Tes Grubbs dengan Python - Statologi\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-28T05:19:18+00:00\",\"dateModified\":\"2023-07-28T05:19:18+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Penjelasan sederhana tentang cara melakukan tes Grubbs dengan Python untuk mendeteksi outlier.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/uji-grubbs-python\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/uji-grubbs-python\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/uji-grubbs-python\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara menjalankan penguji grubbs 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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