{"id":3814,"date":"2023-07-15T09:50:14","date_gmt":"2023-07-15T09:50:14","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e6%88%88%e5%be%b7%e8%8f%b2%e5%b0%94%e5%be%b7%e4%bd%95%e6%97%b6%e6%b5%8b%e8%af%95python\/"},"modified":"2023-07-15T09:50:14","modified_gmt":"2023-07-15T09:50:14","slug":"%e6%88%88%e5%be%b7%e8%8f%b2%e5%b0%94%e5%be%b7%e4%bd%95%e6%97%b6%e6%b5%8b%e8%af%95python","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e6%88%88%e5%be%b7%e8%8f%b2%e5%b0%94%e5%be%b7%e4%bd%95%e6%97%b6%e6%b5%8b%e8%af%95python\/","title":{"rendered":"\u5982\u4f55\u5728 python \u4e2d\u6267\u884c goldfeld-quandt \u6d4b\u8bd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>Goldfeld-Quandt \u68c0\u9a8c<\/strong>\u7528\u4e8e\u786e\u5b9a\u56de\u5f52\u6a21\u578b\u4e2d\u662f\u5426\u5b58\u5728<a href=\"https:\/\/statorials.org\/cn\/\u5f02\u65b9\u5dee\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener\">\u5f02\u65b9\u5dee<\/a>\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5f02\u65b9\u5dee\u662f\u6307\u56de\u5f52\u6a21\u578b\u4e2d<a href=\"https:\/\/statorials.org\/cn\/\u53d8\u91cf\u89e3\u91ca\u6027\u53cd\u5e94\/\" target=\"_blank\" rel=\"noopener\">\u54cd\u5e94\u53d8\u91cf<\/a>\u4e0d\u540c\u6c34\u5e73\u7684<a href=\"https:\/\/statorials.org\/cn\/\u6b8b\u7559\u7269\/\" target=\"_blank\" rel=\"noopener\">\u6b8b\u5dee<\/a>\u79bb\u6563\u7a0b\u5ea6\u4e0d\u5747\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5982\u679c\u5b58\u5728\u5f02\u65b9\u5dee\uff0c\u5219\u8fd9\u8fdd\u53cd\u4e86<a href=\"https:\/\/statorials.org\/cn\/\u7ebf\u6027\u56de\u5f52\u5047\u8bbe\/\" target=\"_blank\" rel=\"noopener\">\u7ebf\u6027\u56de\u5f52\u7684\u5173\u952e\u5047\u8bbe<\/a>\u4e4b\u4e00\uff0c\u5373\u6b8b\u5dee\u5728\u54cd\u5e94\u53d8\u91cf\u7684\u6bcf\u4e2a\u6c34\u5e73\u4e0a\u5747\u5300\u5206\u6563\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u672c\u6559\u7a0b\u63d0\u4f9b\u4e86\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c Goldfeld-Quandt \u6d4b\u8bd5\u7684\u5206\u6b65\u793a\u4f8b\u3002<\/span><\/p>\n<h2><strong>\u7b2c 1 \u6b65\uff1a\u521b\u5efa\u6570\u636e\u96c6<\/strong><\/h2>\n<p><span style=\"color: #000000;\">\u5bf9\u4e8e\u6b64\u793a\u4f8b\uff0c\u8ba9\u6211\u4eec\u521b\u5efa\u4ee5\u4e0b pandas DataFrame\uff0c\u5176\u4e2d\u5305\u542b\u6709\u5173\u73ed\u7ea7 13 \u540d\u5b66\u751f\u7684\u5b66\u4e60\u65f6\u95f4\u3001\u51c6\u5907\u8003\u8bd5\u4ee5\u53ca\u671f\u672b\u8003\u8bd5\u6210\u7ee9\u7684\u4fe1\u606f\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">hours<\/span> ': [1, 2, 2, 4, 2, 1, 5, 4, 2, 4, 4, 3, 6],\n                   ' <span style=\"color: #ff0000;\">exams<\/span> ': [1, 3, 3, 5, 2, 2, 1, 1, 0, 3, 4, 3, 2],\n                   ' <span style=\"color: #ff0000;\">score<\/span> ': [76, 78, 85, 88, 72, 69, 94, 94, 88, 92, 90, 75, 96]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n    hours exam score\n0 1 1 76\n1 2 3 78\n2 2 3 85\n3 4 5 88\n4 2 2 72\n5 1 2 69\n6 5 1 94\n7 4 1 94\n8 2 0 88\n9 4 3 92\n10 4 4 90\n11 3 3 75\n12 6 2 96<\/strong><\/pre>\n<h2><span style=\"color: #000000;\"><strong>\u6b65\u9aa4 2\uff1a\u62df\u5408\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4f7f\u7528<strong>\u5c0f\u65f6\u6570<\/strong>\u548c<strong>\u8003\u8bd5<\/strong>\u4f5c\u4e3a\u9884\u6d4b\u53d8\u91cf\uff0c<strong>\u5206\u6570<\/strong>\u4f5c\u4e3a\u54cd\u5e94\u53d8\u91cf\u6765\u62df\u5408\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> statsmodels. <span style=\"color: #3366ff;\">api<\/span> <span style=\"color: #008000;\">as<\/span> sm\n\n<span style=\"color: #008080;\">#define predictor and response variables\n<\/span>y = df[' <span style=\"color: #ff0000;\">score<\/span> ']\nx = df[[' <span style=\"color: #ff0000;\">hours<\/span> ', ' <span style=\"color: #ff0000;\">exams<\/span> ']]\n\n<span style=\"color: #008080;\">#add constant to predictor variables\n<\/span>x = sm. <span style=\"color: #3366ff;\">add_constant<\/span> (x)\n\n<span style=\"color: #008080;\">#fit linear regression model\n<\/span>model = sm. <span style=\"color: #3366ff;\">OLS<\/span> (y,x). <span style=\"color: #3366ff;\">fit<\/span> ()\n\n<span style=\"color: #008080;\">#view model summary\n<\/span><span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">model.summary<\/span> ())\n\n                            OLS Regression Results                            \n==================================================== ============================\nDept. Variable: R-squared score: 0.718\nModel: OLS Adj. R-squared: 0.661\nMethod: Least Squares F-statistic: 12.70\nDate: Mon, 31 Oct 2022 Prob (F-statistic): 0.00180\nTime: 09:22:56 Log-Likelihood: -38.618\nNo. Observations: 13 AIC: 83.24\nDf Residuals: 10 BIC: 84.93\nModel: 2                                         \nCovariance Type: non-robust                                         \n==================================================== ============================\n                 coef std err t P&gt;|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 71.4048 4.001 17.847 0.000 62.490 80.319\nhours 5.1275 1.018 5.038 0.001 2.860 7.395\nexams -1.2121 1.147 -1.057 0.315 -3.768 1.344\n==================================================== ============================\nOmnibus: 1,103 Durbin-Watson: 1,248\nProb(Omnibus): 0.576 Jarque-Bera (JB): 0.803\nSkew: -0.289 Prob(JB): 0.669\nKurtosis: 1.928 Cond. No. 11.7\n==================================================== ============================\n<\/strong><\/span><\/pre>\n<h2><span style=\"color: #000000;\"><strong>\u6b65\u9aa4 3\uff1a\u6267\u884c Goldfeld-Quandt \u68c0\u9a8c<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4f7f\u7528<strong>statsmodels<\/strong> <strong>het_goldfeldquandt()<\/strong>\u51fd\u6570\u6765\u6267\u884c Goldfeld-Quandt \u6d4b\u8bd5\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8\u610f<\/strong>\uff1aGoldfeld-Quandt \u68c0\u9a8c\u7684\u5de5\u4f5c\u539f\u7406\u662f\u5220\u9664\u4f4d\u4e8e\u6570\u636e\u96c6\u4e2d\u5fc3\u7684\u591a\u4e2a\u89c2\u6d4b\u503c\uff0c\u7136\u540e\u6d4b\u8bd5\u6b8b\u5dee\u5206\u5e03\u662f\u5426\u4e0e\u7ed1\u5b9a\u5728\u4e2d\u5fc3\u89c2\u6d4b\u503c\u4e24\u4fa7\u7684\u4e24\u4e2a\u7ed3\u679c\u6570\u636e\u96c6\u4e0d\u540c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u901a\u5e38\uff0c\u6211\u4eec\u9009\u62e9\u5220\u9664\u603b\u89c2\u6d4b\u503c\u7684\u5927\u7ea6 20%\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>drop<\/strong>\u53c2\u6570\u6765\u6307\u5b9a\u6211\u4eec\u8981\u5220\u9664 20% \u7684\u89c2\u6d4b\u503c\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#perform Goldfeld-Quandt test\n<\/span>sm. <span style=\"color: #3366ff;\">stats<\/span> . <span style=\"color: #3366ff;\">diagnosis<\/span> . <span style=\"color: #3366ff;\">het_goldfeldquandt<\/span> (y, x, drop= <span style=\"color: #008000;\">0.2<\/span> )\n\n(1.7574505407790355, 0.38270288684680076, 'increasing')<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u662f\u5982\u4f55\u89e3\u91ca\u7ed3\u679c\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u68c0\u9a8c\u7edf\u8ba1\u91cf\u4e3a<b>1.757<\/b> \u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u76f8\u5e94\u7684 p \u503c\u4e3a<strong>0.383<\/strong> \u3002<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Goldfeld-Quandt \u68c0\u9a8c\u4f7f\u7528\u4ee5\u4e0b\u539f\u5047\u8bbe\u548c\u5907\u62e9\u5047\u8bbe\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u7a7a(H <sub>0<\/sub> )<\/strong> \uff1a\u5b58\u5728\u540c\u65b9\u5dee\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u66ff\u4ee3\u65b9\u6848 ( <sub>HA<\/sub> )\uff1a<\/strong>\u5b58\u5728\u5f02\u65b9\u5dee\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7531\u4e8e p \u503c\u4e0d\u5c0f\u4e8e 0.05\uff0c\u56e0\u6b64\u6211\u4eec\u65e0\u6cd5\u62d2\u7edd\u539f\u5047\u8bbe\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u6ca1\u6709\u8db3\u591f\u7684\u8bc1\u636e\u8bc1\u660e\u5f02\u65b9\u5dee\u6027\u662f\u56de\u5f52\u6a21\u578b\u4e2d\u7684\u4e00\u4e2a\u95ee\u9898\u3002<\/span><\/p>\n<h2><strong>\u63a5\u4e0b\u6765\u505a\u4ec0\u4e48<\/strong><\/h2>\n<p><span style=\"color: #000000;\">\u5982\u679c\u60a8\u672a\u80fd\u62d2\u7edd Goldfeld-Quandt \u68c0\u9a8c\u7684\u539f\u5047\u8bbe\uff0c\u5219\u4e0d\u5b58\u5728\u5f02\u65b9\u5dee\uff0c\u60a8\u53ef\u4ee5\u7ee7\u7eed\u89e3\u91ca\u539f\u59cb\u56de\u5f52\u7684\u7ed3\u679c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4f46\u662f\uff0c\u5982\u679c\u62d2\u7edd\u96f6\u5047\u8bbe\uff0c\u5219\u610f\u5473\u7740\u6570\u636e\u4e2d\u5b58\u5728\u5f02\u65b9\u5dee\u6027\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u56de\u5f52\u8f93\u51fa\u8868\u4e2d\u663e\u793a\u7684\u6807\u51c6\u8bef\u5dee\u53ef\u80fd\u4e0d\u53ef\u9760\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6709\u51e0\u79cd\u5e38\u89c1\u7684\u65b9\u6cd5\u53ef\u4ee5\u89e3\u51b3\u6b64\u95ee\u9898\uff0c\u5305\u62ec\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. \u53d8\u6362\u54cd\u5e94\u53d8\u91cf\u3002<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u60a8\u53ef\u4ee5\u5c1d\u8bd5\u5bf9\u54cd\u5e94\u53d8\u91cf\u6267\u884c\u8f6c\u6362\uff0c\u4f8b\u5982\u53d6\u54cd\u5e94\u53d8\u91cf\u7684<a href=\"https:\/\/statorials.org\/cn\/\u5728python\u4e2d\u8f6c\u6362\u6570\u636e\/\" target=\"_blank\" rel=\"noopener\">\u5bf9\u6570\u3001\u5e73\u65b9\u6839\u6216\u7acb\u65b9\u6839<\/a>\u3002\u4e00\u822c\u6765\u8bf4\uff0c\u8fd9\u4f1a\u5bfc\u81f4\u5f02\u65b9\u5dee\u6d88\u5931\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2. \u4f7f\u7528\u52a0\u6743\u56de\u5f52\u3002<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u52a0\u6743\u56de\u5f52\u6839\u636e\u62df\u5408\u503c\u7684\u65b9\u5dee\u4e3a\u6bcf\u4e2a\u6570\u636e\u70b9\u5206\u914d\u6743\u91cd\u3002\u672c\u8d28\u4e0a\uff0c\u8fd9\u4e3a\u5177\u6709\u8f83\u9ad8\u65b9\u5dee\u7684\u6570\u636e\u70b9\u8d4b\u4e88\u4e86\u8f83\u4f4e\u7684\u6743\u91cd\uff0c\u4ece\u800c\u51cf\u5c11\u4e86\u5b83\u4eec\u7684\u6b8b\u5dee\u5e73\u65b9\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5f53\u4f7f\u7528\u9002\u5f53\u7684\u6743\u91cd\u65f6\uff0c\u52a0\u6743\u56de\u5f52\u53ef\u4ee5\u6d88\u9664\u5f02\u65b9\u5dee\u95ee\u9898\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><a href=\"https:\/\/statorials.org\/cn\/ols\u56de\u5f52-python\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c OLS \u56de\u5f52<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/python-\u6b8b\u5dee\u56fe\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 Python \u4e2d\u521b\u5efa\u6b8b\u5dee\u56fe<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/python\u4e2d\u7684\u767d\u8272\u6d4b\u8bd5\/\">\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u6000\u7279\u68c0\u9a8c<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/breusch\u5f02\u6559\u5f92\u6d4b\u8bd5python\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c Breusch-Pagan \u6d4b\u8bd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Goldfeld-Quandt \u68c0\u9a8c\u7528\u4e8e\u786e\u5b9a\u56de\u5f52\u6a21\u578b\u4e2d\u662f\u5426\u5b58\u5728\u5f02\u65b9\u5dee\u3002 \u5f02\u65b9\u5dee\u662f\u6307\u56de\u5f52\u6a21\u578b\u4e2d\u54cd\u5e94\u53d8\u91cf\u4e0d\u540c\u6c34\u5e73\u7684 [&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-3814","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 Goldfeld-Quandt \u68c0\u9a8c \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u901a\u8fc7\u4e00\u4e2a\u793a\u4f8b\u8bf4\u660e\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c Goldfeld-Quandt \u6d4b\u8bd5\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\/\u6208\u5fb7\u83f2\u5c14\u5fb7\u4f55\u65f6\u6d4b\u8bd5python\/\" \/>\n<meta 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