{"id":4506,"date":"2023-07-10T14:11:32","date_gmt":"2023-07-10T14:11:32","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e6%b2%83%e5%b0%94%e5%be%b7%e6%b5%8b%e8%af%95%e8%9f%92%e8%9b%87\/"},"modified":"2023-07-10T14:11:32","modified_gmt":"2023-07-10T14:11:32","slug":"%e6%b2%83%e5%b0%94%e5%be%b7%e6%b5%8b%e8%af%95%e8%9f%92%e8%9b%87","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e6%b2%83%e5%b0%94%e5%be%b7%e6%b5%8b%e8%af%95%e8%9f%92%e8%9b%87\/","title":{"rendered":"\u5982\u4f55\u5728 python \u4e2d\u6267\u884c wald \u6d4b\u8bd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>Wald \u68c0\u9a8c<\/strong>\u53ef\u7528\u4e8e\u6d4b\u8bd5\u6a21\u578b\u7684\u4e00\u4e2a\u6216\u591a\u4e2a\u53c2\u6570\u662f\u5426\u7b49\u4e8e\u7279\u5b9a\u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b64\u68c0\u9a8c\u901a\u5e38\u7528\u4e8e\u786e\u5b9a\u56de\u5f52\u6a21\u578b\u4e2d\u7684\u4e00\u4e2a\u6216\u591a\u4e2a\u9884\u6d4b\u53d8\u91cf\u662f\u5426\u7b49\u4e8e\u96f6\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u5728\u6b64\u6d4b\u8bd5\u4e2d\u4f7f\u7528\u4ee5\u4e0b\u539f\u5047\u8bbe\u548c\u5907\u62e9<a href=\"https:\/\/statorials.org\/cn\/\u5047\u8bbe\u68c0\u9a8c1\/\" target=\"_blank\" rel=\"noopener\">\u5047\u8bbe<\/a>\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>H <sub>0<\/sub><\/strong> \uff1a\u67d0\u4e9b\u9884\u6d4b\u53d8\u91cf\u96c6\u5168\u90e8\u7b49\u4e8e\u96f6\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>H <sub>A<\/sub><\/strong> \uff1a\u5e76\u975e\u96c6\u5408\u4e2d\u7684\u6240\u6709\u9884\u6d4b\u53d8\u91cf\u90fd\u7b49\u4e8e 0\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u5982\u679c\u6211\u4eec\u65e0\u6cd5\u62d2\u7edd\u539f\u5047\u8bbe\uff0c\u90a3\u4e48\u6211\u4eec\u53ef\u4ee5\u4ece\u6a21\u578b\u4e2d\u5220\u9664\u6307\u5b9a\u7684\u9884\u6d4b\u53d8\u91cf\u96c6\uff0c\u56e0\u4e3a\u5b83\u4eec\u4e0d\u4f1a\u5728\u6a21\u578b\u62df\u5408\u65b9\u9762\u63d0\u4f9b\u7edf\u8ba1\u4e0a\u663e\u7740\u7684\u6539\u8fdb\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c Wald \u6d4b\u8bd5<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b\uff1aPython \u4e2d\u7684 Wald \u6d4b\u8bd5<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u5bf9\u4e8e\u8fd9\u4e2a\u4f8b\u5b50\uff0c\u6211\u4eec\u5c06\u4f7f\u7528\u8457\u540d\u7684<strong>mtcars<\/strong>\u6570\u636e\u96c6\u6765\u62df\u5408\u4ee5\u4e0b\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\">mpg = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub>\u53ef\u7528 + \u03b2 <sub>2<\/sub>\u78b3\u6c34\u5316\u5408\u7269 + \u03b2 <sub>3<\/sub>\u9a6c\u529b + \u03b2 <sub>4<\/sub>\u6c7d\u7f38<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u4e86\u5982\u4f55\u62df\u5408\u6b64\u56de\u5f52\u6a21\u578b\u5e76\u663e\u793a\u6a21\u578b\u6458\u8981\uff1a<\/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;\">\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>statsmodels<\/strong> <strong>wald_test()<\/strong>\u51fd\u6570\u6765\u6d4b\u8bd5\u9884\u6d4b\u53d8\u91cf\u201chp\u201d\u548c\u201ccyl\u201d\u7684\u56de\u5f52\u7cfb\u6570\u662f\u5426\u90fd\u7b49\u4e8e 0\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4e0b\u9762\u7684\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u9645\u4e2d\u4f7f\u7528\u8fd9\u4e2a\u51fd\u6570\uff1a<\/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;\">\u4ece\u7ed3\u679c\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u68c0\u9a8c\u7684<a href=\"https:\/\/statorials.org\/cn\/p\u503c\u7edf\u8ba1\u663e\u7740\u6027\/\" target=\"_blank\" rel=\"noopener\">p \u503c\u4e3a<\/a><strong>0.414<\/strong> \u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7531\u4e8e\u8be5 p \u503c\u4e0d\u5c0f\u4e8e 0.05\uff0c\u56e0\u6b64\u6211\u4eec\u65e0\u6cd5\u62d2\u7edd Wald \u68c0\u9a8c\u7684\u539f\u5047\u8bbe\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8fd9\u610f\u5473\u7740\u6211\u4eec\u53ef\u4ee5\u5047\u8bbe\u9884\u6d4b\u53d8\u91cf\u201chp\u201d\u548c\u201ccyl\u201d\u7684\u56de\u5f52\u7cfb\u6570\u5747\u4e3a\u96f6\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4ece\u6a21\u578b\u4e2d\u5220\u9664\u8fd9\u4e9b\u9879\uff0c\u56e0\u4e3a\u5b83\u4eec\u5728\u7edf\u8ba1\u4e0a\u4e0d\u4f1a\u663e\u7740\u6539\u5584\u6574\u4f53\u6a21\u578b\u62df\u5408\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u64cd\u4f5c\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/cn\/python-\u4e2d\u7684\u7b80\u5355\u7ebf\u6027\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u6267\u884c\u7b80\u5355\u7ebf\u6027\u56de\u5f52<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u591a\u9879\u5f0f\u56de\u5f52-python\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u591a\u9879\u5f0f\u56de\u5f52<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u5982\u4f55\u5728python\u4e2d\u8ba1\u7b97vive\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u7528Python\u8ba1\u7b97VIF<\/a><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Wald \u68c0\u9a8c\u53ef\u7528\u4e8e\u6d4b\u8bd5\u6a21\u578b\u7684\u4e00\u4e2a\u6216\u591a\u4e2a\u53c2\u6570\u662f\u5426\u7b49\u4e8e\u7279\u5b9a\u503c\u3002 \u6b64\u68c0\u9a8c\u901a\u5e38\u7528\u4e8e\u786e\u5b9a\u56de\u5f52\u6a21\u578b\u4e2d\u7684\u4e00\u4e2a\u6216\u591a\u4e2a\u9884\u6d4b\u53d8\u91cf [&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":[],"class_list":["post-4506","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c Wald \u68c0\u9a8c \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c Wald \u6d4b\u8bd5\uff0c\u5305\u62ec\u4e00\u4e2a\u5b8c\u6574\u7684\u793a\u4f8b\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\/cn\/\u6c83\u5c14\u5fb7\u6d4b\u8bd5\u87d2\u86c7\/\" \/>\n<meta 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