{"id":1249,"date":"2023-07-27T03:48:04","date_gmt":"2023-07-27T03:48:04","guid":{"rendered":"https:\/\/statorials.org\/cn\/python%e4%b8%ad%e7%9a%84%e5%ad%a6%e7%94%9f%e5%8c%96%e6%ae%8b%e5%9f%ba\/"},"modified":"2023-07-27T03:48:04","modified_gmt":"2023-07-27T03:48:04","slug":"python%e4%b8%ad%e7%9a%84%e5%ad%a6%e7%94%9f%e5%8c%96%e6%ae%8b%e5%9f%ba","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/python%e4%b8%ad%e7%9a%84%e5%ad%a6%e7%94%9f%e5%8c%96%e6%ae%8b%e5%9f%ba\/","title":{"rendered":"\u5982\u4f55\u5728python\u4e2d\u8ba1\u7b97\u5b66\u751f\u5316\u6b8b\u5dee"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>\u5b66\u751f\u6b8b\u5dee<\/strong>\u53ea\u662f\u6b8b\u5dee\u9664\u4ee5\u5176\u4f30\u8ba1\u7684\u6807\u51c6\u5dee\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5728\u5b9e\u8df5\u4e2d\uff0c\u6211\u4eec\u901a\u5e38\u8bf4\u6570\u636e\u96c6\u4e2d\u5b66\u751f\u6b8b\u5dee\u7edd\u5bf9\u503c\u5927\u4e8e 3 \u7684\u4efb\u4f55<a href=\"https:\/\/statorials.org\/cn\/\u7edf\u8ba1\u89c2\u5bdf\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u89c2\u6d4b\u503c<\/a>\u90fd\u662f\u5f02\u5e38\u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 statsmodels \u7684<a href=\"https:\/\/www.statsmodels.org\/stable\/generated\/statsmodels.regression.linear_model.OLSResults.outlier_test.html\" target=\"_blank\" rel=\"noopener noreferrer\">OLSResults.outlier_test()<\/a>\u51fd\u6570\u5feb\u901f\u83b7\u53d6 Python \u4e2d\u56de\u5f52\u6a21\u578b\u7684\u5b66\u751f\u5316\u6b8b\u5dee\uff0c\u8be5\u51fd\u6570\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>OLSResults.outlier_test()<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u5176\u4e2d<i>OLSResults<\/i>\u662f\u4f7f\u7528 statsmodels <strong>ols()<\/strong>\u51fd\u6570\u62df\u5408\u7684\u7ebf\u6027\u6a21\u578b\u7684\u540d\u79f0\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b\uff1aPython \u4e2d\u5b66\u751f\u5316\u6b8b\u5dee\u7684\u8ba1\u7b97<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u6211\u4eec\u7528 Python \u6784\u5efa\u4ee5\u4e0b<a href=\"https:\/\/statorials.org\/cn\/python-\u4e2d\u7684\u7b80\u5355\u7ebf\u6027\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u7b80\u5355\u7ebf\u6027\u56de\u5f52<\/a>\u6a21\u578b\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#import necessary packages and functions\n<\/span><span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n<span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\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;\">from<\/span> statsmodels. <span style=\"color: #3366ff;\">formula<\/span> . <span style=\"color: #3366ff;\">api<\/span> <span style=\"color: #008000;\">import<\/span> ols\n\n<span style=\"color: #008080;\">#create dataset<\/span>\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({'rating': [90, 85, 82, 88, 94, 90, 76, 75, 87, 86],\n                   'points': [25, 20, 14, 16, 27, 20, 12, 15, 14, 19]})\n\n<span style=\"color: #008080;\">#fit simple linear regression model<\/span>\nmodel = ols('rating ~ points', data=df). <span style=\"color: #3366ff;\">fit<\/span> ()\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>outlier_test()<\/strong>\u51fd\u6570\u751f\u6210\u4e00\u4e2a DataFrame\uff0c\u5176\u4e2d\u5305\u542b\u6570\u636e\u96c6\u4e2d\u6bcf\u4e2a\u89c2\u5bdf\u503c\u7684\u5b66\u751f\u5316\u6b8b\u5dee\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate studentized residuals<\/span>\nstud_res = model. <span style=\"color: #3366ff;\">outlier_test<\/span> ()\n\n<span style=\"color: #008080;\">#display studentized residuals<\/span>\nprint(stud_res)\n\n    student_resid unadj_p bonf(p)\n0 -0.486471 0.641494 1.000000\n1 -0.491937 0.637814 1.000000\n2 0.172006 0.868300 1.000000\n3 1.287711 0.238781 1.000000\n4 0.106923 0.917850 1.000000\n5 0.748842 0.478355 1.000000\n6 -0.968124 0.365234 1.000000\n7 -2.409911 0.046780 0.467801\n8 1.688046 0.135258 1.000000\n9 -0.014163 0.989095 1.000000\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b64 DataFrame \u663e\u793a\u6570\u636e\u96c6\u4e2d\u6bcf\u4e2a\u89c2\u5bdf\u503c\u7684\u4ee5\u4e0b\u503c\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u5b66\u751f\u5316\u6b8b\u7559<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5b66\u751f\u5316\u6b8b\u5dee\u7684\u672a\u8c03\u6574 p \u503c<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5b66\u751f\u6b8b\u5dee\u7684 Bonferroni \u6821\u6b63 p \u503c<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u6570\u636e\u96c6\u4e2d\u7b2c\u4e00\u4e2a\u89c2\u5bdf\u7684\u5b66\u751f\u5316\u6b8b\u5dee\u662f<strong>-0.486471<\/strong> \uff0c\u7b2c\u4e8c\u4e2a\u89c2\u5bdf\u7684\u5b66\u751f\u5316\u6b8b\u5dee\u662f<strong>-0.491937<\/strong> \uff0c\u4f9d\u6b64\u7c7b\u63a8\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u8fd8\u53ef\u4ee5\u521b\u5efa\u9884\u6d4b\u53d8\u91cf\u503c\u4e0e\u76f8\u5e94\u5b66\u751f\u5316\u6b8b\u5dee\u7684\u5feb\u901f\u7ed8\u56fe\uff1a<\/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> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt<\/span>\n\n#define predictor variable values and studentized residuals\n<\/span>x = df[' <span style=\"color: #008000;\">points<\/span> ']\ny = stud_res[' <span style=\"color: #008000;\">student_resid<\/span> ']\n\n<span style=\"color: #008080;\">#create scatterplot of predictor variable vs. studentized residuals\n<\/span>plt. <span style=\"color: #3366ff;\">scatter<\/span> (x,y)\nplt. <span style=\"color: #3366ff;\">axhline<\/span> (y=0, color=' <span style=\"color: #008000;\">black<\/span> ', linestyle=' <span style=\"color: #008000;\">--<\/span> ')\nplt. <span style=\"color: #3366ff;\">xlabel<\/span> (' <span style=\"color: #008000;\">Points<\/span> ')\nplt. <span style=\"color: #3366ff;\">ylabel<\/span> (' <span style=\"color: #008000;\">Studentized Residuals<\/span> ') \n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-12339 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/etudiants1.png\" alt=\"Python \u4e2d\u7684\u5b66\u751f\u5316\u6b8b\u5dee\" width=\"372\" height=\"250\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u4ece\u56fe\u4e2d\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0c\u6ca1\u6709\u4e00\u4e2a\u89c2\u6d4b\u503c\u7684\u5b66\u751f\u6b8b\u5dee\u7edd\u5bf9\u503c\u5927\u4e8e 3\uff0c\u56e0\u6b64\u6570\u636e\u96c6\u4e2d\u4e0d\u5b58\u5728\u660e\u663e\u7684\u5f02\u5e38\u503c\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n<p><a href=\"https:\/\/statorials.org\/cn\/python-\u4e2d\u7684\u7b80\u5355\u7ebf\u6027\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u7b80\u5355\u7ebf\u6027\u56de\u5f52<\/a><br \/> <a href=\"https:\/\/statorials.org\/cn\/\u7ebf\u6027\u56de\u5f52-python\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u591a\u5143\u7ebf\u6027\u56de\u5f52<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/python-\u6b8b\u5dee\u56fe\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u5982\u4f55\u5728 Python \u4e2d\u521b\u5efa\u6b8b\u5dee\u56fe<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u5b66\u751f\u6b8b\u5dee\u53ea\u662f\u6b8b\u5dee\u9664\u4ee5\u5176\u4f30\u8ba1\u7684\u6807\u51c6\u5dee\u3002 \u5728\u5b9e\u8df5\u4e2d\uff0c\u6211\u4eec\u901a\u5e38\u8bf4\u6570\u636e\u96c6\u4e2d\u5b66\u751f\u6b8b\u5dee\u7edd\u5bf9\u503c\u5927\u4e8e 3 \u7684\u4efb\u4f55\u89c2\u6d4b\u503c\u90fd\u662f\u5f02\u5e38 [&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-1249","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\u5728Python\u4e2d\u8ba1\u7b97\u5b66\u751f\u5316\u6b8b\u5dee<\/title>\n<meta name=\"description\" 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