{"id":3229,"date":"2023-07-18T13:59:04","date_gmt":"2023-07-18T13:59:04","guid":{"rendered":"https:\/\/statorials.org\/id\/uji-normalitas-python\/"},"modified":"2023-07-18T13:59:04","modified_gmt":"2023-07-18T13:59:04","slug":"uji-normalitas-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/uji-normalitas-python\/","title":{"rendered":"Cara menguji normalitas dengan python (4 metode)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">Banyak uji statistik <a href=\"https:\/\/statorials.org\/id\/hipotesis-normalitas\/\" target=\"_blank\" rel=\"noopener\">mengasumsikan<\/a> bahwa kumpulan data terdistribusi normal.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ada empat cara umum untuk memeriksa hipotesis ini dengan Python:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. (Metode visual) Membuat histogram.<\/strong><\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Jika histogram kira-kira berbentuk \u201clonceng\u201d, maka data diasumsikan terdistribusi normal.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>2. (Metode visual) Buat plot QQ.<\/strong><\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Jika titik-titik pada plot terletak kira-kira sepanjang garis lurus diagonal, maka data diasumsikan berdistribusi normal.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>3. (Uji statistik formal) Lakukan uji Shapiro-Wilk.<\/strong><\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Jika p-value uji lebih besar dari \u03b1 = 0,05 maka data diasumsikan berdistribusi normal.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>4. (Uji statistik formal) Lakukan uji Kolmogorov-Smirnov.<\/strong><\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Jika p-value uji lebih besar dari \u03b1 = 0,05 maka data diasumsikan berdistribusi normal.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Contoh berikut menunjukkan cara menggunakan masing-masing metode ini dalam praktik.<\/span><\/p>\n<h3> <strong><span style=\"color: #000000;\">Metode 1: Buat Histogram<\/span><\/strong><\/h3>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara membuat histogram untuk kumpulan data yang mengikuti <a href=\"https:\/\/statorials.org\/id\/distribusi-log-python-normal\/\" target=\"_blank\" rel=\"noopener\">distribusi log-normal<\/a> :<\/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> math\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n<span style=\"color: #008000;\">from<\/span> scipy. <span style=\"color: #3366ff;\">stats<\/span> <span style=\"color: #008000;\">import<\/span> lognorm\n<span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n\n<span style=\"color: #008080;\">#make this example reproducible\n<\/span>n.p. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#generate dataset that contains 1000 log-normal distributed values\n<\/span>lognorm_dataset = lognorm. <span style=\"color: #3366ff;\">rvs<\/span> (s=.5, scale= <span style=\"color: #3366ff;\">math.exp<\/span> (1), size=1000)\n\n<span style=\"color: #008080;\">#create histogram to visualize values in dataset\n<\/span>plt. <span style=\"color: #3366ff;\">hist<\/span> (lognorm_dataset, edgecolor=' <span style=\"color: #ff0000;\">black<\/span> ', bins=20)<\/span><\/span><\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-27387 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/normalitepython1.jpg\" alt=\"\" width=\"559\" height=\"364\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">Hanya dengan melihat histogram ini, kita dapat mengetahui bahwa kumpulan data tidak menunjukkan \u201cbentuk lonceng\u201d dan tidak terdistribusi secara normal.<\/span><\/p>\n<h3> <strong><span style=\"color: #000000;\">Metode 2: Buat Plot QQ<\/span><\/strong><\/h3>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara membuat plot QQ untuk kumpulan data yang mengikuti distribusi log-normal:<\/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> math\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n<span style=\"color: #008000;\">from<\/span> scipy. <span style=\"color: #3366ff;\">stats<\/span> <span style=\"color: #008000;\">import<\/span> lognorm\n<span style=\"color: #008000;\">import<\/span> statsmodels. <span style=\"color: #3366ff;\">api<\/span> <span style=\"color: #008000;\">as<\/span> sm\n<span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n\n<span style=\"color: #008080;\">#make this example reproducible\n<\/span>n.p. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#generate dataset that contains 1000 log-normal distributed values\n<\/span>lognorm_dataset = lognorm. <span style=\"color: #3366ff;\">rvs<\/span> (s=.5, scale= <span style=\"color: #3366ff;\">math.exp<\/span> (1), size=1000)\n\n<span style=\"color: #008080;\">#create QQ plot with 45-degree line added to plot\n<\/span>fig = sm. <span style=\"color: #3366ff;\">qqplot<\/span> (lognorm_dataset, line=' <span style=\"color: #ff0000;\">45<\/span> ')\n\nplt. <span style=\"color: #3366ff;\">show<\/span> ()\n<\/span><\/span><\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-27390 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/normalitepython2.jpg\" alt=\"\" width=\"533\" height=\"359\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">Jika titik plot terletak kira-kira di sepanjang garis lurus diagonal, secara umum kita berasumsi bahwa kumpulan data terdistribusi normal.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Namun titik-titik pada grafik ini jelas tidak sesuai dengan garis merah, sehingga kita tidak dapat berasumsi bahwa kumpulan data ini berdistribusi normal.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ini seharusnya masuk akal mengingat kami menghasilkan data menggunakan fungsi distribusi log-normal.<\/span><\/p>\n<h3> <strong><span style=\"color: #000000;\">Metode 3: Lakukan tes Shapiro-Wilk<\/span><\/strong><\/h3>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara melakukan Shapiro-Wilk untuk kumpulan data yang mengikuti distribusi log-normal:<\/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> math\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n<span style=\"color: #008000;\">from<\/span> scipy.stats <span style=\"color: #008000;\">import<\/span> shapiro \n<span style=\"color: #008000;\">from<\/span> scipy. <span style=\"color: #3366ff;\">stats<\/span> <span style=\"color: #008000;\">import<\/span> lognorm\n\n<span style=\"color: #008080;\">#make this example reproducible\n<\/span>n.p. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#generate dataset that contains 1000 log-normal distributed values\n<\/span>lognorm_dataset = lognorm. <span style=\"color: #3366ff;\">rvs<\/span> (s=.5, scale= <span style=\"color: #3366ff;\">math.exp<\/span> (1), size=1000)\n\n<span style=\"color: #008080;\">#perform Shapiro-Wilk test for normality\n<\/span>shapiro(lognorm_dataset)\n\nShapiroResult(statistic=0.8573324680328369, pvalue=3.880663073872444e-29)\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Dari hasilnya, kita dapat melihat bahwa statistik pengujian adalah <strong>0,857<\/strong> dan nilai p yang sesuai adalah <strong>3,88e-29<\/strong> (sangat mendekati nol).<\/span><\/p>\n<p> <span style=\"color: #000000;\">Karena nilai p kurang dari 0,05, kami menolak hipotesis nol uji Shapiro-Wilk.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Artinya kita mempunyai cukup bukti untuk mengatakan bahwa data sampel tidak berasal dari distribusi normal.<\/span><\/p>\n<h3> <strong><span style=\"color: #000000;\">Metode 4: Lakukan tes Kolmogorov-Smirnov<\/span><\/strong><\/h3>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara melakukan pengujian Kolmogorov-Smirnov untuk kumpulan data yang mengikuti distribusi log-normal:<\/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> math\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n<span style=\"color: #008000;\">from<\/span> scipy.stats <span style=\"color: #008000;\">import<\/span> kstest\n<span style=\"color: #008000;\">from<\/span> scipy. <span style=\"color: #3366ff;\">stats<\/span> <span style=\"color: #008000;\">import<\/span> lognorm\n\n<span style=\"color: #008080;\">#make this example reproducible\n<\/span>n.p. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#generate dataset that contains 1000 log-normal distributed values\n<\/span>lognorm_dataset = lognorm. <span style=\"color: #3366ff;\">rvs<\/span> (s=.5, scale= <span style=\"color: #3366ff;\">math.exp<\/span> (1), size=1000)\n\n<span style=\"color: #008080;\">#perform Kolmogorov-Smirnov test for normality\n<\/span>kstest(lognorm_dataset, ' <span style=\"color: #ff0000;\">norm<\/span> ')\n\nKstestResult(statistic=0.84125708308077, pvalue=0.0)\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Dari hasilnya, kita dapat melihat bahwa statistik uji adalah <strong>0,841<\/strong> dan nilai p yang sesuai adalah <strong>0,0<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Karena nilai p kurang dari 0,05, kami menolak hipotesis nol uji Kolmogorov-Smirnov.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Artinya kita mempunyai cukup bukti untuk mengatakan bahwa data sampel tidak berasal dari distribusi normal.<\/span><\/p>\n<h3> <strong>Cara menangani data yang tidak normal<\/strong><\/h3>\n<p> <span style=\"color: #000000;\">Jika kumpulan data tertentu <em>tidak<\/em> terdistribusi normal, kita sering kali dapat melakukan salah satu transformasi berikut untuk membuatnya lebih terdistribusi normal:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. Transformasi log:<\/strong> ubah nilai x menjadi <strong>log(x)<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2. Transformasi akar kuadrat:<\/strong> Ubah nilai x menjadi <strong><span style=\"border-top: 1px solid black;\">\u221ax<\/span><\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>3. Transformasi akar pangkat tiga:<\/strong> ubah nilai x menjadi <strong>x <sup>1\/3<\/sup><\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Dengan melakukan transformasi ini, kumpulan data secara umum menjadi lebih terdistribusi secara normal.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Baca <a href=\"https:\/\/statorials.org\/id\/mengubah-data-dengan-python\/\" target=\"_blank\" rel=\"noopener noreferrer\">tutorial ini<\/a> untuk melihat cara melakukan transformasi ini dengan Python.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Banyak uji statistik mengasumsikan bahwa kumpulan data terdistribusi normal. Ada empat cara umum untuk memeriksa hipotesis ini dengan Python: 1. (Metode visual) Membuat histogram. Jika histogram kira-kira berbentuk \u201clonceng\u201d, maka data diasumsikan terdistribusi normal. 2. (Metode visual) Buat plot QQ. Jika titik-titik pada plot terletak kira-kira sepanjang garis lurus diagonal, maka data diasumsikan berdistribusi normal. [&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 Menguji Normalitas dengan Python (4 Metode) - Statologi<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara menguji normalitas dengan Python, 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\/uji-normalitas-python\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cara Menguji Normalitas dengan Python (4 Metode) - Statologi\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara menguji normalitas dengan Python, dengan beberapa contoh.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/uji-normalitas-python\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-18T13:59:04+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/normalitepython1.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=\"3 menit\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/id\/uji-normalitas-python\/\",\"url\":\"https:\/\/statorials.org\/id\/uji-normalitas-python\/\",\"name\":\"Cara Menguji Normalitas dengan Python (4 Metode) - Statologi\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-18T13:59:04+00:00\",\"dateModified\":\"2023-07-18T13:59:04+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara menguji normalitas dengan Python, dengan beberapa contoh.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/uji-normalitas-python\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/uji-normalitas-python\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/uji-normalitas-python\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara menguji normalitas dengan python (4 metode)\"}]},{\"@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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