{"id":1339,"date":"2023-07-26T19:53:42","date_gmt":"2023-07-26T19:53:42","guid":{"rendered":"https:\/\/statorials.org\/cn\/python%e4%b8%ad%e7%9a%84%e6%ae%8b%e5%b7%ae%e5%b9%b3%e6%96%b9%e5%92%8c\/"},"modified":"2023-07-26T19:53:42","modified_gmt":"2023-07-26T19:53:42","slug":"python%e4%b8%ad%e7%9a%84%e6%ae%8b%e5%b7%ae%e5%b9%b3%e6%96%b9%e5%92%8c","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/python%e4%b8%ad%e7%9a%84%e6%ae%8b%e5%b7%ae%e5%b9%b3%e6%96%b9%e5%92%8c\/","title":{"rendered":"\u5982\u4f55\u7528 python \u8ba1\u7b97\u6b8b\u5dee\u5e73\u65b9\u548c"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/cn\/\u6b8b\u7559\u7269\/\" target=\"_blank\" rel=\"noopener\">\u6b8b\u5dee<\/a>\u662f\u56de\u5f52\u6a21\u578b\u4e2d\u89c2\u6d4b\u503c\u4e0e\u9884\u6d4b\u503c\u4e4b\u95f4\u7684\u5dee\u5f02\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8ba1\u7b97\u65b9\u6cd5\u5982\u4e0b\uff1a<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b8b\u5dee = \u89c2\u6d4b\u503c \u2013 \u9884\u6d4b\u503c<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4e86\u89e3\u56de\u5f52\u6a21\u578b\u5bf9\u6570\u636e\u96c6\u7684\u62df\u5408\u7a0b\u5ea6\u7684\u4e00\u79cd\u65b9\u6cd5\u662f\u8ba1\u7b97<strong>\u6b8b\u5dee\u5e73\u65b9\u548c<\/strong>\uff0c\u8ba1\u7b97\u516c\u5f0f\u5982\u4e0b\uff1a<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b8b\u5dee\u5e73\u65b9\u548c = \u03a3(e <sub>i<\/sub> ) <sup>2<\/sup><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\u5b50\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\u03a3<\/strong> \uff1a\u5e0c\u814a\u7b26\u53f7\uff0c\u610f\u601d\u662f\u201c\u548c\u201d<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>e <sub>i<\/sub><\/strong> : \u7b2c i<sup>\u4e2a<\/sup>\u6b8b\u57fa<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u503c\u8d8a\u4f4e\uff0c\u6a21\u578b\u8d8a\u9002\u5408\u6570\u636e\u96c6\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u672c\u6559\u7a0b\u63d0\u4f9b\u4e86\u5982\u4f55\u5728 Python \u4e2d\u8ba1\u7b97\u56de\u5f52\u6a21\u578b\u7684\u6b8b\u5dee\u5e73\u65b9\u548c\u7684\u5206\u6b65\u793a\u4f8b\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c 1 \u6b65\uff1a\u8f93\u5165\u6570\u636e<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u5728\u6b64\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u5c06\u8f93\u5165\u4e0e 14 \u540d\u4e0d\u540c\u5b66\u751f\u7684\u5b66\u4e60\u5c0f\u65f6\u6570\u3001\u53c2\u52a0\u7684\u9884\u5907\u8003\u8bd5\u603b\u6570\u4ee5\u53ca\u8003\u8bd5\u6210\u7ee9\u76f8\u5173\u7684\u6570\u636e\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> pandas <span style=\"color: #107d3f;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame<\/span>\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #008000;\">hours<\/span> ': [1, 2, 2, 4, 2, 1, 5, 4, 2, 4, 4, 3, 6, 5],\n                   ' <span style=\"color: #008000;\">exams<\/span> ': [1, 3, 3, 5, 2, 2, 1, 1, 0, 3, 4, 3, 2, 4],\n                   ' <span style=\"color: #008000;\">score<\/span> ': [76, 78, 85, 88, 72, 69, 94, 94, 88, 92, 90, 75, 96, 90]})\n<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u6b65\u9aa4 2\uff1a\u62df\u5408\u56de\u5f52\u6a21\u578b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4f7f\u7528<\/span>statsmodels \u5e93\u4e2d\u7684<a href=\"https:\/\/www.statsmodels.org\/devel\/generated\/statsmodels.regression.linear_model.OLS.html\" target=\"_blank\" rel=\"noopener noreferrer\">OLS() \u51fd\u6570<\/a><span style=\"color: #000000;\">\u6267\u884c\u666e\u901a\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52\uff0c\u4f7f\u7528\u201c\u5c0f\u65f6\u201d\u548c\u201c\u8003\u8bd5\u201d\u4f5c\u4e3a\u9884\u6d4b\u53d8\u91cf\uff0c\u201c\u5206\u6570\u201d\u4f5c\u4e3a\u54cd\u5e94\u53d8\u91cf\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; 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 response variable\n<\/span>y = df[' <span style=\"color: #008000;\">score<\/span> ']\n\n<span style=\"color: #008080;\">#define predictor variables\n<\/span>x = df[[' <span style=\"color: #008000;\">hours<\/span> ', ' <span style=\"color: #008000;\">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: #993300;\">print<\/span> ( <span style=\"color: #3366ff;\">model.summary<\/span> ())\n\n                            OLS Regression Results                            \n==================================================== ============================\nDept. Variable: R-squared score: 0.722\nModel: OLS Adj. R-squared: 0.671\nMethod: Least Squares F-statistic: 14.27\nDate: Sat, 02 Jan 2021 Prob (F-statistic): 0.000878\nTime: 15:58:35 Log-Likelihood: -41.159\nNo. Comments: 14 AIC: 88.32\nDf Residuals: 11 BIC: 90.24\nModel: 2                                         \nCovariance Type: non-robust                                         \n==================================================== ============================\n                 coef std err t P&gt;|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 71.8144 3.680 19.517 0.000 63.716 79.913\nhours 5.0318 0.942 5.339 0.000 2.958 7.106\nexams -1.3186 1.063 -1.240 0.241 -3.658 1.021\n==================================================== ============================\nOmnibus: 0.976 Durbin-Watson: 1.270\nProb(Omnibus): 0.614 Jarque-Bera (JB): 0.757\nSkew: -0.245 Prob(JB): 0.685\nKurtosis: 1.971 Cond. No. 12.1\n==================================================== ============================\n<\/strong><\/span><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u6b65\u9aa43\uff1a\u8ba1\u7b97\u6b8b\u5dee\u5e73\u65b9\u548c<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4e0b\u9762\u7684\u4ee3\u7801\u6765\u8ba1\u7b97\u6a21\u578b\u7684\u6b8b\u5dee\u5e73\u65b9\u548c\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #993300;\">print<\/span> ( <span style=\"color: #3366ff;\">model.ssr<\/span> )\n\n293.25612951525414\n<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u6b8b\u5dee\u5e73\u65b9\u548c\u4e3a<strong>293,256<\/strong> \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\">\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\">\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u591a\u5143\u7ebf\u6027\u56de\u5f52<\/a><br \/>\u6b8b\u5dee\u5e73\u65b9\u548c\u8ba1\u7b97\u5668<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6b8b\u5dee\u662f\u56de\u5f52\u6a21\u578b\u4e2d\u89c2\u6d4b\u503c\u4e0e\u9884\u6d4b\u503c\u4e4b\u95f4\u7684\u5dee\u5f02\u3002 \u8ba1\u7b97\u65b9\u6cd5\u5982\u4e0b\uff1a \u6b8b\u5dee = \u89c2\u6d4b\u503c \u2013 \u9884\u6d4b\u503c \u4e86\u89e3\u56de\u5f52\u6a21\u578b\u5bf9\u6570\u636e\u96c6 [&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-1339","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - 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