{"id":869,"date":"2023-07-28T11:57:38","date_gmt":"2023-07-28T11:57:38","guid":{"rendered":"https:\/\/statorials.org\/cn\/breusch%e5%bc%82%e6%95%99%e5%be%92%e6%b5%8b%e8%af%95python\/"},"modified":"2023-07-28T11:57:38","modified_gmt":"2023-07-28T11:57:38","slug":"breusch%e5%bc%82%e6%95%99%e5%be%92%e6%b5%8b%e8%af%95python","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/breusch%e5%bc%82%e6%95%99%e5%be%92%e6%b5%8b%e8%af%95python\/","title":{"rendered":"\u5982\u4f55\u5728 python \u4e2d\u6267\u884c breusch-pagan \u6d4b\u8bd5"},"content":{"rendered":"

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\u5728\u56de\u5f52\u5206\u6790\u4e2d\uff0c\u5f02\u65b9\u5dee<\/a>\u662f\u6307\u6b8b\u5dee\u7684\u79bb\u6563\u7a0b\u5ea6\u4e0d\u5747\u5300\u3002\u66f4\u51c6\u786e\u5730\u8bf4\uff0c\u8fd9\u662f\u6b8b\u5dee\u5206\u5e03\u5728\u6d4b\u91cf\u503c\u8303\u56f4\u5185\u53d1\u751f\u7cfb\u7edf\u53d8\u5316\u7684\u60c5\u51b5\u3002<\/span><\/p>\n

\u5f02\u65b9\u5dee\u662f\u4e00\u4e2a\u95ee\u9898\uff0c\u56e0\u4e3a\u666e\u901a\u6700\u5c0f\u4e8c\u4e58 (OLS) \u56de\u5f52\u5047\u8bbe\u6b8b\u5dee\u6765\u81ea\u5177\u6709\u540c\u65b9\u5dee<\/em>\u7684\u603b\u4f53\uff0c\u5373\u65b9\u5dee\u6052\u5b9a\u3002<\/span><\/p>\n

\u5f53\u56de\u5f52\u5206\u6790\u4e2d\u5b58\u5728\u5f02\u65b9\u5dee\u65f6\uff0c\u5206\u6790\u7ed3\u679c\u53d8\u5f97\u96be\u4ee5\u76f8\u4fe1\u3002<\/span><\/p>\n

\u786e\u5b9a\u56de\u5f52\u5206\u6790<\/a>\u4e2d\u662f\u5426\u5b58\u5728\u5f02\u65b9\u5dee\u7684\u4e00\u79cd\u65b9\u6cd5\u662f\u4f7f\u7528Breusch-Pagan \u68c0\u9a8c<\/a>\u3002<\/strong><\/span><\/p>\n

\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c Breusch-Pagan \u6d4b\u8bd5\u3002<\/span><\/p>\n

\u793a\u4f8b\uff1aPython \u4e2d\u7684 Breusch-Pagan \u6d4b\u8bd5<\/strong><\/span><\/h2>\n

\u5728\u6b64\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u5c06\u4f7f\u7528\u4ee5\u4e0b\u6570\u636e\u96c6\u6765\u63cf\u8ff0 10 \u540d\u7bee\u7403\u8fd0\u52a8\u5458\u7684\u5c5e\u6027\uff1a<\/span><\/p>\n

 import<\/span> numpy as<\/span> np\nimport<\/span> pandas as<\/span> pd\n\n#create dataset<\/span>\ndf = pd.DataFrame({'rating': [90, 85, 82, 88, 94, 90, 76, 75, 87, 86],\n                   'points': [25, 20, 14, 16, 27, 20, 12, 15, 14, 19],\n                   'assists': [5, 7, 7, 8, 5, 7, 6, 9, 9, 5],\n                   'rebounds': [11, 8, 10, 6, 6, 9, 6, 10, 10, 7]})\n\n#view dataset\n<\/span>df\n\n\trating points assists rebounds\n0 90 25 5 11\n1 85 20 7 8\n2 82 14 7 10\n3 88 16 8 6\n4 94 27 5 6\n5 90 20 7 9\n6 76 12 6 6\n7 75 15 9 10\n8 87 14 9 10\n9 86 19 5 7<\/strong><\/pre>\n
\u6211\u4eec\u5c06\u62df\u5408\u4e00\u4e2a\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff0c\u4f7f\u7528\u5f97\u5206\u4f5c\u4e3a\u54cd\u5e94\u53d8\u91cf\uff0c\u5f97\u5206\u3001\u52a9\u653b\u548c\u7bee\u677f\u4f5c\u4e3a\u89e3\u91ca\u53d8\u91cf\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u6267\u884c Breusch-Pagan \u68c0\u9a8c\u6765\u786e\u5b9a\u56de\u5f52\u4e2d\u662f\u5426\u5b58\u5728\u5f02\u65b9\u5dee\u3002<\/span><\/p>\n
\u6b65\u9aa4 1\uff1a\u62df\u5408\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u3002<\/strong><\/span><\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u5c06\u62df\u5408\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n
 import<\/span> statsmodels.formula.api as<\/span> smf\n\n#fit regression model<\/span>\nfit = smf.ols('rating ~ points+assists+rebounds', data=df). fit<\/span> ()\n\n#view model summary\n<\/span>print<\/span> (fit.summary())\n<\/strong><\/pre>\n
\u6b65\u9aa4 2\uff1a\u6267\u884c Breusch-Pagan \u6d4b\u8bd5\u3002<\/strong><\/span><\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u6267\u884c Breusch-Pagan \u68c0\u9a8c\u4ee5\u786e\u5b9a\u662f\u5426\u5b58\u5728\u5f02\u65b9\u5dee\u3002<\/span><\/p>\n
 from<\/span> statsmodels.compat import<\/span> lzip\nimport<\/span> statsmodels.stats.api as<\/span> sms\n\n#perform Bresuch-Pagan test<\/span>\nnames = ['Lagrange multiplier statistic', 'p-value',\n        'f-value', 'f p-value']\ntest = sms.het_breuschpagan(fit.resid, fit.model.exog)\n\nlzip(names, test)\n\n[('Lagrange multiply statistic', 6.003951995818433),\n ('p-value', 0.11141811013399583),\n ('f-value', 3.004944880309618),\n ('f p-value', 0.11663863538255281)]<\/strong><\/pre>\n
 Breusch-Pagan \u68c0\u9a8c\u4f7f\u7528\u4ee5\u4e0b\u539f\u5047\u8bbe\u548c\u5907\u62e9\u5047\u8bbe\uff1a<\/span><\/p>\n
\u539f\u5047\u8bbe (H 0<\/sub> )\uff1a<\/strong>\u5b58\u5728\u540c\u65b9\u5dee\u6027\u3002<\/span><\/p>\n
\u5907\u62e9\u5047\u8bbe\uff1a(Ha)\uff1a<\/strong>\u4e0d<\/em>\u5b58\u5728\u540c\u65b9\u5dee\uff08\u5373\u5b58\u5728\u5f02\u65b9\u5dee\uff09<\/span><\/p>\n
\u5728\u6b64\u793a\u4f8b\u4e2d\uff0c\u68c0\u9a8c\u7684\u62c9\u683c\u6717\u65e5\u4e58\u5b50\u7edf\u8ba1\u91cf\u4e3a6.004<\/strong> \uff0c\u76f8\u5e94\u7684 p \u503c\u4e3a0.1114<\/strong> \u3002\u7531\u4e8e\u8be5 p \u503c\u4e0d\u5c0f\u4e8e 0.05\uff0c\u56e0\u6b64\u6211\u4eec\u65e0\u6cd5\u62d2\u7edd\u539f\u5047\u8bbe\u3002\u6211\u4eec\u6ca1\u6709\u8db3\u591f\u7684\u8bc1\u636e\u8868\u660e\u56de\u5f52\u6a21\u578b\u4e2d\u5b58\u5728\u5f02\u65b9\u5dee\u6027\u3002<\/span><\/p>\n
\u5982\u4f55\u4fee\u590d\u5f02\u65b9\u5dee<\/strong><\/span><\/h2>\n
\u5728\u524d\u9762\u7684\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u770b\u5230\u56de\u5f52\u6a21\u578b\u4e2d\u4e0d\u5b58\u5728\u5f02\u65b9\u5dee\u6027\u3002<\/span><\/p>\n
\u7136\u800c\uff0c\u5f53\u5f02\u65b9\u5dee\u786e\u5b9e\u5b58\u5728\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u4e09\u79cd\u5e38\u89c1\u65b9\u6cd5\u6765\u7ea0\u6b63\u8fd9\u79cd\u60c5\u51b5\uff1a<\/span><\/p>\n
 1.<\/strong>\u53d8\u6362\u56e0\u53d8\u91cf\u3002<\/strong>\u6821\u6b63\u5f02\u65b9\u5dee\u7684\u4e00\u79cd\u65b9\u6cd5\u662f\u4ee5\u67d0\u79cd\u65b9\u5f0f\u8f6c\u6362\u56e0\u53d8\u91cf\u3002\u5e38\u89c1\u7684\u8f6c\u6362\u662f\u7b80\u5355\u5730\u53d6\u56e0\u53d8\u91cf\u7684\u5bf9\u6570\u3002<\/span><\/p>\n
 2. \u91cd\u65b0\u5b9a\u4e49\u56e0\u53d8\u91cf\u3002<\/strong>\u7ea0\u6b63\u5f02\u65b9\u5dee\u7684\u53e6\u4e00\u79cd\u65b9\u6cd5\u662f\u91cd\u65b0\u5b9a\u4e49\u56e0\u53d8\u91cf\u3002\u4e00\u79cd\u5e38\u89c1\u7684\u65b9\u6cd5\u662f\u4f7f\u7528\u56e0\u53d8\u91cf\u7684\u6bd4\u7387<\/em>\uff0c\u800c\u4e0d\u662f\u539f\u59cb\u503c\u3002<\/span><\/p>\n
 3. \u4f7f\u7528\u52a0\u6743\u56de\u5f52\u3002<\/strong>\u6821\u6b63\u5f02\u65b9\u5dee\u7684\u53e6\u4e00\u79cd\u65b9\u6cd5\u662f\u4f7f\u7528\u52a0\u6743\u56de\u5f52\u3002\u8fd9\u79cd\u7c7b\u578b\u7684\u56de\u5f52\u6839\u636e\u62df\u5408\u503c\u7684\u65b9\u5dee\u4e3a\u6bcf\u4e2a\u6570\u636e\u70b9\u5206\u914d\u6743\u91cd\u3002\u5f53\u4f7f\u7528\u9002\u5f53\u7684\u6743\u91cd\u65f6\uff0c\u53ef\u4ee5\u6d88\u9664\u5f02\u65b9\u5dee\u95ee\u9898\u3002<\/span><\/p>\n
\u5728\u56de\u5f52\u5206\u6790\u4e2d\uff0c\u5f02\u65b9\u5dee\u662f\u6307\u6b8b\u5dee\u7684\u79bb\u6563\u7a0b\u5ea6\u4e0d\u5747\u5300\u3002\u66f4\u51c6\u786e\u5730\u8bf4\uff0c\u8fd9\u662f\u6b8b\u5dee\u5206\u5e03\u5728\u6d4b\u91cf\u503c\u8303\u56f4\u5185\u53d1\u751f\u7cfb\u7edf\u53d8\u5316\u7684\u60c5\u51b5\u3002 \u5f02\u65b9 […]<\/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-869","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c Breusch-Pagan \u6d4b\u8bd5 - Statorials<\/title>\n<meta name=\"description\" content=\"\u5173\u4e8e\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u548c\u89e3\u91ca Breusch-Pagan \u6d4b\u8bd5\u7684\u7b80\u5355\u8bf4\u660e\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\/breusch\u5f02\u6559\u5f92\u6d4b\u8bd5python\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c Breusch-Pagan \u6d4b\u8bd5 - 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