{"id":4500,"date":"2023-07-10T14:11:32","date_gmt":"2023-07-10T14:11:32","guid":{"rendered":"https:\/\/statorials.org\/ja\/wald-%e3%83%86%e3%82%b9%e3%83%88-python\/"},"modified":"2023-07-10T14:11:32","modified_gmt":"2023-07-10T14:11:32","slug":"wald-%e3%83%86%e3%82%b9%e3%83%88-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/wald-%e3%83%86%e3%82%b9%e3%83%88-python\/","title":{"rendered":"Python \u3067 wald \u30c6\u30b9\u30c8\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>Wald \u30c6\u30b9\u30c8\u3092<\/strong>\u4f7f\u7528\u3057\u3066\u3001\u30e2\u30c7\u30eb\u306e 1 \u3064\u4ee5\u4e0a\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u30fc\u304c\u7279\u5b9a\u306e\u5024\u306b\u7b49\u3057\u3044\u304b\u3069\u3046\u304b\u3092\u30c6\u30b9\u30c8\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30c6\u30b9\u30c8\u306f\u3001\u56de\u5e30\u30e2\u30c7\u30eb\u5185\u306e 1 \u3064\u4ee5\u4e0a\u306e\u4e88\u6e2c\u5909\u6570\u304c\u30bc\u30ed\u306b\u7b49\u3057\u3044\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3059\u308b\u305f\u3081\u306b\u3088\u304f\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u691c\u5b9a\u3067\u306f\u6b21\u306e\u5e30\u7121\u4eee\u8aac\u3068\u5bfe\u7acb<a href=\"https:\/\/statorials.org\/ja\/\u4eee\u8aac\u691c\u8a3c1\/\" target=\"_blank\" rel=\"noopener\">\u4eee\u8aac\u3092<\/a>\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>H <sub>0<\/sub><\/strong> : \u4e88\u6e2c\u5b50\u5909\u6570\u306e\u4e00\u90e8\u306e\u30bb\u30c3\u30c8\u306f\u3059\u3079\u3066\u30bc\u30ed\u306b\u7b49\u3057\u3044\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>H <sub>A<\/sub><\/strong> : \u30bb\u30c3\u30c8\u5185\u306e\u3059\u3079\u3066\u306e\u4e88\u6e2c\u5b50\u5909\u6570\u304c\u30bc\u30ed\u306b\u7b49\u3057\u3044\u308f\u3051\u3067\u306f\u3042\u308a\u307e\u305b\u3093\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u5e30\u7121\u4eee\u8aac\u3092\u68c4\u5374\u3067\u304d\u306a\u3044\u5834\u5408\u306f\u3001\u30e2\u30c7\u30eb\u306e\u9069\u5408\u6027\u306b\u304a\u3044\u3066\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u6539\u5584\u304c\u5f97\u3089\u308c\u306a\u3044\u305f\u3081\u3001\u6307\u5b9a\u3055\u308c\u305f\u4e88\u6e2c\u5909\u6570\u306e\u30bb\u30c3\u30c8\u3092\u30e2\u30c7\u30eb\u304b\u3089\u524a\u9664\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u306f\u3001Python \u3067 Wald \u30c6\u30b9\u30c8\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u4f8b: Python \u3067\u306e Wald \u30c6\u30b9\u30c8<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u3053\u306e\u4f8b\u3067\u306f\u3001\u6709\u540d\u306a<strong>mtcars<\/strong>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u3066\u3001\u6b21\u306e\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">mpg = \u03b2 <sub>0<\/sub> + \u5229\u7528\u53ef\u80fd\u306a\u03b2 <sub>1<\/sub> + \u03b2 <sub>2<\/sub>\u70ad\u6c34\u5316\u7269 + \u03b2 <sub>3<\/sub>\u99ac\u529b + \u03b2 <sub>4<\/sub>\u30b7\u30ea\u30f3\u30c0\u30fc<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u3053\u306e\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u3066\u30e2\u30c7\u30eb\u306e\u6982\u8981\u3092\u8868\u793a\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/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;\">\u6b21\u306b\u3001 <strong>statsmodels<\/strong> <strong>wald_test()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u4e88\u6e2c\u5b50\u5909\u6570\u300chp\u300d\u3068\u300ccyl\u300d\u306e\u56de\u5e30\u4fc2\u6570\u304c\u4e21\u65b9\u3068\u3082 0 \u306b\u7b49\u3057\u3044\u304b\u3069\u3046\u304b\u3092\u30c6\u30b9\u30c8\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u3053\u306e\u95a2\u6570\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/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;\">\u7d50\u679c\u304b\u3089\u3001\u691c\u5b9a\u306e<a href=\"https:\/\/statorials.org\/ja\/p-\u5024\u306e\u7d71\u8a08\u7684\u6709\u610f\u6027\/\" target=\"_blank\" rel=\"noopener\">p \u5024\u306f<\/a><strong>0.414<\/strong>\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e p \u5024\u306f 0.05 \u672a\u6e80\u3067\u306f\u306a\u3044\u305f\u3081\u3001Wald \u691c\u5b9a\u306e\u5e30\u7121\u4eee\u8aac\u3092\u68c4\u5374\u3067\u304d\u307e\u305b\u3093\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u308c\u306f\u3001\u4e88\u6e2c\u5909\u6570\u300chp\u300d\u3068\u300ccyl\u300d\u306e\u56de\u5e30\u4fc2\u6570\u304c\u4e21\u65b9\u3068\u3082\u30bc\u30ed\u306b\u7b49\u3057\u3044\u3068\u4eee\u5b9a\u3067\u304d\u308b\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u308c\u3089\u306e\u9805\u306f\u30e2\u30c7\u30eb\u5168\u4f53\u306e\u9069\u5408\u6027\u3092\u7d71\u8a08\u7684\u306b\u5927\u5e45\u306b\u6539\u5584\u3057\u306a\u3044\u305f\u3081\u3001\u30e2\u30c7\u30eb\u304b\u3089\u524a\u9664\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u4ed6\u306e\u4e00\u822c\u7684\u306a\u64cd\u4f5c\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ja\/python\u3066\u3099\u306e\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\/\" target=\"_blank\" rel=\"noopener\">\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u591a\u9805\u5f0f\u56de\u5e30python\/\" target=\"_blank\" rel=\"noopener\">Python \u3067\u591a\u9805\u5f0f\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/python\u3066\u3099vive\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\/\" target=\"_blank\" rel=\"noopener\">Python \u3067 VIF \u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Wald \u30c6\u30b9\u30c8\u3092\u4f7f\u7528\u3057\u3066\u3001\u30e2\u30c7\u30eb\u306e 1 \u3064\u4ee5\u4e0a\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u30fc\u304c\u7279\u5b9a\u306e\u5024\u306b\u7b49\u3057\u3044\u304b\u3069\u3046\u304b\u3092\u30c6\u30b9\u30c8\u3067\u304d\u307e\u3059\u3002 \u3053\u306e\u30c6\u30b9\u30c8\u306f\u3001\u56de\u5e30\u30e2\u30c7\u30eb\u5185\u306e 1 \u3064\u4ee5\u4e0a\u306e\u4e88\u6e2c\u5909\u6570\u304c\u30bc\u30ed\u306b\u7b49\u3057\u3044\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3059\u308b\u305f\u3081\u306b\u3088\u304f\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002 \u3053\u306e [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-4500","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python \u3067 Wald \u30c6\u30b9\u30c8\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 \u2013 Statology<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u5b8c\u5168\u306a\u4f8b\u3092\u542b\u3081\u3066\u3001Python \u3067 Wald \u30c6\u30b9\u30c8\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\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\/ja\/wald-\u30c6\u30b9\u30c8-python\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python \u3067 Wald \u30c6\u30b9\u30c8\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 \u2013 Statology\" \/>\n<meta property=\"og:description\" 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