{"id":4503,"date":"2023-07-10T14:11:32","date_gmt":"2023-07-10T14:11:32","guid":{"rendered":"https:\/\/statorials.org\/id\/uji-wald-python\/"},"modified":"2023-07-10T14:11:32","modified_gmt":"2023-07-10T14:11:32","slug":"uji-wald-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/uji-wald-python\/","title":{"rendered":"Cara melakukan tes wald dengan python"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>Uji Wald<\/strong> dapat digunakan untuk menguji apakah satu atau lebih parameter suatu model sama dengan nilai tertentu.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Pengujian ini sering digunakan untuk menentukan apakah satu atau lebih variabel prediktor dalam suatu model regresi sama dengan nol.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Kami menggunakan hipotesis nol dan <a href=\"https:\/\/statorials.org\/id\/pengujian-hipotesis-1\/\" target=\"_blank\" rel=\"noopener\">hipotesis<\/a> alternatif berikut untuk pengujian ini:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>H <sub>0<\/sub><\/strong> : Beberapa himpunan variabel prediktor semuanya sama dengan nol.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>H <sub>A<\/sub><\/strong> : Tidak semua variabel prediktor dalam himpunan sama dengan nol.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Jika kita gagal menolak hipotesis nol, maka kita dapat menghapus kumpulan variabel prediktor tertentu dari model, karena variabel tersebut tidak memberikan peningkatan yang signifikan secara statistik dalam kesesuaian model.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Contoh berikut menunjukkan cara melakukan tes Wald dengan Python<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>Contoh: Tes Wald dengan Python<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Untuk contoh ini, kita akan menggunakan kumpulan data <strong>mtcars<\/strong> yang terkenal agar sesuai dengan model regresi linier berganda berikut:<\/span><\/p>\n<p> <span style=\"color: #000000;\">mpg = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub> tersedia + \u03b2 <sub>2<\/sub> karbohidrat + \u03b2 <sub>3<\/sub> hp + \u03b2 <sub>4<\/sub> silinder<\/span><\/p>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara menyesuaikan model regresi ini dan menampilkan ringkasan model:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> statsmodels. <span style=\"color: #3366ff;\">formula<\/span> . <span style=\"color: #3366ff;\">api<\/span> <span style=\"color: #008000;\">as<\/span> smf\n<span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">import<\/span> io\n\n<span style=\"color: #008080;\">#define dataset as string\n<\/span>mtcars_data=\"\"\"model,mpg,cyl,disp,hp,drat,wt,qsec,vs,am,gear,carb\nMazda RX4,21,6,160,110,3.9,2.62,16.46,0,1,4,4\nMazda RX4 Wag,21.6,160,110,3.9,2.875,17.02,0,1,4,4\nDatsun 710,22.8,4,108,93,3.85,2.32,18.61,1,1,4,1\nHornet 4 Drive,21.4,6,258,110,3.08,3.215,19.44,1,0,3,1\nHornet Sportabout,18.7,8,360,175,3.15,3.44,17.02,0,0,3,2\nValiant,18.1,6,225,105,2.76,3.46,20.22,1,0,3,1\nDuster 360,14.3,8,360,245,3.21,3.57,15.84,0,0,3,4\nMerc 240D,24.4,4,146.7,62,3.69,3.19,20,1,0,4,2\nMerc 230,22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2\nMerc 280,19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4\nMerc 280C,17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4\nMerc 450SE,16.4,8,275.8,180,3.07,4.07,17.4,0,0,3,3\nMerc 450SL,17.3,8,275.8,180,3.07,3.73,17.6,0,0,3,3\nMerc 450SLC,15.2,8,275.8,180,3.07,3.78,18,0,0,3,3\nCadillac Fleetwood,10.4,8,472,205,2.93,5.25,17.98,0,0,3,4\nLincoln Continental,10.4,8,460,215,3,5.424,17.82,0,0,3,4\nChrysler Imperial,14.7,8,440,230,3.23,5.345,17.42,0,0,3,4\nFiat 128,32.4,4,78.7,66,4.08,2.2,19.47,1,1,4,1\nHonda Civic,30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2\nToyota Corolla,33.9,4,71.1,65,4.22,1.835,19.9,1,1,4,1\nToyota Corona,21.5,4,120.1,97,3.7,2.465,20.01,1,0,3,1\nDodge Challenger,15.5,8,318,150,2.76,3.52,16.87,0,0,3,2\nAMC Javelin,15.2,8,304,150,3.15,3.435,17.3,0,0,3,2\nCamaro Z28,13.3,8,350,245,3.73,3.84,15.41,0,0,3,4\nPontiac Firebird,19.2,8,400,175,3.08,3.845,17.05,0,0,3,2\nFiat X1-9,27.3,4,79,66,4.08,1.935,18.9,1,1,4,1\nPorsche 914-2,26,4,120.3,91,4.43,2.14,16.7,0,1,5,2\nLotus Europa,30.4,4,95.1,113,3.77,1.513,16.9,1,1,5,2\nFord Pantera L,15.8,8,351,264,4.22,3.17,14.5,0,1,5,4\nFerrari Dino,19.7,6,145,175,3.62,2.77,15.5,0,1,5,6\nMaserati Bora,15.8,301,335,3.54,3.57,14.6,0,1,5,8\nVolvo 142E,21.4,4,121,109,4.11,2.78,18.6,1,1,4,2\"\"\"\n\n<span style=\"color: #008080;\">#convert string to DataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">read_csv<\/span> ( <span style=\"color: #3366ff;\">io.StringIO<\/span> (mtcars_data), sep=\" <span style=\"color: #ff0000;\">,<\/span> \")\n\n<span style=\"color: #008080;\">#fit multiple linear regression model\n<\/span>results = smf. <span style=\"color: #3366ff;\">ols<\/span> (' <span style=\"color: #ff0000;\">mpg~disp+carb+hp+cyl<\/span> ',df). <span style=\"color: #3366ff;\">fit<\/span> ()\n\n<span style=\"color: #008080;\">#view regression model summary\n<\/span>results. <span style=\"color: #3366ff;\">summary<\/span> ()\n\n\tcoef std err t P&gt;|t| [0.025 0.975]\nIntercept34.0216 2.523 13.482 0.000 28.844 39.199\navailable -0.0269 0.011 -2.379 0.025 -0.050 -0.004\ncarb -0.9269 0.579 -1.601 0.121 -2.115 0.261\nhp 0.0093 0.021 0.452 0.655 -0.033 0.052\ncyl -1.0485 0.784 -1.338 0.192 -2.657 0.560\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Selanjutnya, kita dapat menggunakan fungsi <strong>statsmodels<\/strong> <strong>wald_test()<\/strong> untuk menguji apakah koefisien regresi untuk variabel prediktor &#8220;hp&#8221; dan &#8220;cyl&#8221; keduanya sama dengan nol.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara menggunakan fungsi ini dalam praktiknya:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#perform Wald Test to determine if 'hp' and 'cyl' coefficients are both zero<\/span>\n<span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">results.wald_test<\/span> (' <span style=\"color: #ff0000;\">(hp=0, cyl=0)<\/span> '))\n\nF test: F=array([[0.91125429]]), p=0.41403001184235005, df_denom=27, df_num=2\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Dari hasil tersebut terlihat bahwa <a href=\"https:\/\/statorials.org\/id\/p-menghargai-signifikansi-statistik\/\" target=\"_blank\" rel=\"noopener\">p-value<\/a> pengujian tersebut adalah <strong>0,414<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Karena nilai p ini tidak kurang dari 0,05, kita gagal menolak hipotesis nol uji Wald.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Artinya kita dapat berasumsi bahwa koefisien regresi untuk variabel prediktor \u201chp\u201d dan \u201ccyl\u201d keduanya sama dengan nol.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Kami dapat menghapus istilah-istilah ini dari model karena secara statistik istilah-istilah tersebut tidak meningkatkan kesesuaian model secara keseluruhan secara signifikan.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>Sumber daya tambahan<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Tutorial berikut menjelaskan cara melakukan operasi umum lainnya dengan Python:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/id\/regresi-linier-sederhana-dengan-python\/\" target=\"_blank\" rel=\"noopener\">Cara melakukan regresi linier sederhana<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/python-regresi-polinomial\/\" target=\"_blank\" rel=\"noopener\">Cara melakukan regresi polinomial dengan Python<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/cara-menghitung-vive-dengan-python\/\" target=\"_blank\" rel=\"noopener\">Cara menghitung VIF dengan Python<\/a><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Uji Wald dapat digunakan untuk menguji apakah satu atau lebih parameter suatu model sama dengan nilai tertentu. Pengujian ini sering digunakan untuk menentukan apakah satu atau lebih variabel prediktor dalam suatu model regresi sama dengan nol. Kami menggunakan hipotesis nol dan hipotesis alternatif berikut untuk pengujian ini: H 0 : Beberapa himpunan variabel prediktor semuanya [&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 Wald dengan Python \u2013 Statologi<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara melakukan tes Wald dengan Python, termasuk contoh lengkapnya.\" \/>\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-wald-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 Wald dengan Python \u2013 Statologi\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara melakukan tes Wald dengan Python, termasuk contoh lengkapnya.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/uji-wald-python\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-10T14:11:32+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-wald-python\/\",\"url\":\"https:\/\/statorials.org\/id\/uji-wald-python\/\",\"name\":\"Cara Melakukan Tes Wald dengan Python \u2013 Statologi\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-10T14:11:32+00:00\",\"dateModified\":\"2023-07-10T14:11:32+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara melakukan tes Wald dengan Python, termasuk contoh lengkapnya.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/uji-wald-python\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/uji-wald-python\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/uji-wald-python\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara melakukan tes wald 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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