{"id":3819,"date":"2023-07-15T09:08:37","date_gmt":"2023-07-15T09:08:37","guid":{"rendered":"https:\/\/statorials.org\/cn\/python%e4%b8%ad%e7%9a%84%e6%9c%80%e5%b0%8f%e5%8a%a0%e6%9d%83%e5%b9%b3%e6%96%b9\/"},"modified":"2023-07-15T09:08:37","modified_gmt":"2023-07-15T09:08:37","slug":"python%e4%b8%ad%e7%9a%84%e6%9c%80%e5%b0%8f%e5%8a%a0%e6%9d%83%e5%b9%b3%e6%96%b9","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/python%e4%b8%ad%e7%9a%84%e6%9c%80%e5%b0%8f%e5%8a%a0%e6%9d%83%e5%b9%b3%e6%96%b9\/","title":{"rendered":"\u5982\u4f55\u5728 python \u4e2d\u6267\u884c\u52a0\u6743\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52"},"content":{"rendered":"

<\/p>\n

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\u7ebf\u6027\u56de\u5f52\u7684\u5173\u952e\u5047\u8bbe<\/a>\u4e4b\u4e00\u662f\u6b8b\u5dee<\/a>\u5728\u9884\u6d4b\u53d8\u91cf\u7684\u6bcf\u4e2a\u6c34\u5e73\u4e0a\u4ee5\u76f8\u7b49\u65b9\u5dee\u5206\u5e03\u3002\u8fd9\u79cd\u5047\u8bbe\u79f0\u4e3a\u540c\u65b9\u5dee\u6027<\/strong>\u3002<\/span><\/p>\n

\u5982\u679c\u4e0d\u9075\u5b88\u6b64\u5047\u8bbe\uff0c\u5219\u79f0\u6b8b\u5dee\u4e2d\u5b58\u5728\u5f02\u65b9\u5dee\u6027<\/a>\u3002\u5f53\u8fd9\u79cd\u60c5\u51b5\u53d1\u751f\u65f6\uff0c\u56de\u5f52\u7ed3\u679c\u53d8\u5f97\u4e0d\u53ef\u9760\u3002<\/span><\/p>\n

\u89e3\u51b3\u6b64\u95ee\u9898\u7684\u4e00\u79cd\u65b9\u6cd5\u662f\u4f7f\u7528\u52a0\u6743\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52<\/strong>\uff0c\u5b83\u4e3a\u89c2\u6d4b\u503c<\/a>\u5206\u914d\u6743\u91cd\uff0c\u4f7f\u5f97\u8bef\u5dee\u65b9\u5dee\u8f83\u5c0f\u7684\u89c2\u6d4b\u503c\u83b7\u5f97\u66f4\u591a\u6743\u91cd\uff0c\u56e0\u4e3a\u4e0e\u8bef\u5dee\u65b9\u5dee\u8f83\u5927\u7684\u89c2\u6d4b\u503c\u76f8\u6bd4\uff0c\u5b83\u4eec\u5305\u542b\u66f4\u591a\u4fe1\u606f\u3002<\/span><\/p>\n

\u672c\u6559\u7a0b\u63d0\u4f9b\u4e86\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u52a0\u6743\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52\u7684\u5206\u6b65\u793a\u4f8b\u3002<\/span><\/p>\n

\u7b2c 1 \u6b65\uff1a\u521b\u5efa\u6570\u636e<\/strong><\/span><\/h2>\n

\u9996\u5148\uff0c\u6211\u4eec\u521b\u5efa\u4ee5\u4e0b pandas DataFrame\uff0c\u5176\u4e2d\u5305\u542b\u6709\u5173\u73ed\u7ea7 16 \u540d\u5b66\u751f\u7684\u5b66\u4e60\u5c0f\u65f6\u6570\u548c\u671f\u672b\u8003\u8bd5\u6210\u7ee9\u7684\u4fe1\u606f\uff1a<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' hours<\/span> ': [1, 1, 2, 2, 2, 3, 4, 4, 4, 5, 5, 5, 6, 6, 7, 8],\n                   ' score<\/span> ': [48, 78, 72, 70, 66, 92, 93, 75, 75, 80, 95, 97,\n                             90, 96, 99, 99]})\n\n#view first five rows of DataFrame\n<\/span>print<\/span> ( df.head<\/span> ())\n\n   hours score\n0 1 48\n1 1 78\n2 2 72\n3 2 70\n4 2 66<\/strong><\/pre>\n
\u6b65\u9aa4 2\uff1a\u62df\u5408\u7b80\u5355\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/strong><\/span><\/h2>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4f7f\u7528statsmodels<\/strong>\u6a21\u5757\u4e2d\u7684\u51fd\u6570\u6765\u62df\u5408\u4e00\u4e2a\u7b80\u5355\u7684\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff0c\u4f7f\u7528\u5c0f\u65f6<\/strong>\u4f5c\u4e3a\u9884\u6d4b\u53d8\u91cf\uff0c\u5f97\u5206<\/strong>\u4f5c\u4e3a\u54cd\u5e94\u53d8\u91cf\uff1a<\/span><\/p>\n
 import<\/span> statsmodels.api as<\/span> sm\n\n#define predictor and response variables\n<\/span>y = df[' score<\/span> ']\nX = df[' hours<\/span> ']\n\n#add constant to predictor variables\n<\/span>X = sm. add_constant<\/span> (x)\n\n#fit linear regression model\n<\/span>fit = sm. OLS<\/span> (y,x). fit<\/span> ()\n\n#view model summary\n<\/span>print<\/span> ( fit.summary<\/span> ())\n\n                            OLS Regression Results                            \n==================================================== ============================\nDept. Variable: R-squared score: 0.630\nModel: OLS Adj. R-squared: 0.603\nMethod: Least Squares F-statistic: 23.80\nDate: Mon, 31 Oct 2022 Prob (F-statistic): 0.000244\nTime: 11:19:54 Log-Likelihood: -57.184\nNo. Observations: 16 AIC: 118.4\nDf Residuals: 14 BIC: 119.9\nModel: 1                                         \nCovariance Type: non-robust                                         \n==================================================== ============================\n                 coef std err t P>|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 60.4669 5.128 11.791 0.000 49.468 71.465\nhours 5.5005 1.127 4.879 0.000 3.082 7.919\n==================================================== ============================\nOmnibus: 0.041 Durbin-Watson: 1.910\nProb(Omnibus): 0.980 Jarque-Bera (JB): 0.268\nSkew: -0.010 Prob(JB): 0.875\nKurtosis: 2.366 Cond. No. 10.5<\/strong><\/pre>\n
\u4ece\u6a21\u578b\u6458\u8981\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u6a21\u578b\u7684 R \u5e73\u65b9\u503c\u4e3a0.630<\/strong> \u3002<\/span><\/p>\n
\u6b65\u9aa4 3\uff1a\u62df\u5408\u52a0\u6743\u6700\u5c0f\u4e8c\u4e58\u6a21\u578b<\/strong><\/span><\/h2>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528statsmodels<\/strong> WLS()<\/strong>\u51fd\u6570\u901a\u8fc7\u8bbe\u7f6e\u6743\u91cd\u6765\u6267\u884c\u52a0\u6743\u6700\u5c0f\u4e8c\u4e58\u6cd5\uff0c\u4ee5\u4f7f\u65b9\u5dee\u8f83\u4f4e\u7684\u89c2\u6d4b\u503c\u83b7\u5f97\u66f4\u5927\u7684\u6743\u91cd\uff1a<\/span><\/p>\n
 #define weights to use\n<\/span>wt = 1\/smf. ols<\/span> (' fit.resid.abs() ~ fit.fittedvalues<\/span> ', data=df). fit<\/span> (). fitted values<\/span> **2\n\n#fit weighted least squares regression model\n<\/span>fit_wls = sm. WLS<\/span> (y, X, weights=wt). fit<\/span> ()\n\n#view summary of weighted least squares regression model\n<\/span>print<\/span> ( fit_wls.summary<\/span> ())\n\n                            WLS Regression Results                            \n==================================================== ============================\nDept. Variable: R-squared score: 0.676\nModel: WLS Adj. R-squared: 0.653\nMethod: Least Squares F-statistic: 29.24\nDate: Mon, 31 Oct 2022 Prob (F-statistic): 9.24e-05\nTime: 11:20:10 Log-Likelihood: -55.074\nNo. Comments: 16 AIC: 114.1\nDf Residuals: 14 BIC: 115.7\nModel: 1                                         \nCovariance Type: non-robust                                         \n==================================================== ============================\n                 coef std err t P>|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 63.9689 5.159 12.400 0.000 52.905 75.033\nhours 4.7091 0.871 5.407 0.000 2.841 6.577\n==================================================== ============================\nOmnibus: 2,482 Durbin-Watson: 1,786\nProb(Omnibus): 0.289 Jarque-Bera (JB): 1.058\nSkew: 0.029 Prob(JB): 0.589\nKurtosis: 1.742 Cond. No. 17.6\n==================================================== ============================<\/strong><\/span><\/pre>\n
\u4ece\u7ed3\u679c\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u8be5\u52a0\u6743\u6700\u5c0f\u4e8c\u4e58\u6a21\u578b\u7684 R \u5e73\u65b9\u503c\u5df2\u589e\u52a0\u81f30.676<\/strong> \u3002<\/span><\/p>\n
\u8fd9\u8868\u660e\u52a0\u6743\u6700\u5c0f\u4e8c\u4e58\u6a21\u578b\u6bd4\u7b80\u5355\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u80fd\u591f\u89e3\u91ca\u66f4\u591a\u7684\u8003\u8bd5\u6210\u7ee9\u65b9\u5dee\u3002<\/span><\/p>\n
\u8fd9\u544a\u8bc9\u6211\u4eec\uff0c\u4e0e\u7b80\u5355\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u76f8\u6bd4\uff0c\u52a0\u6743\u6700\u5c0f\u4e8c\u4e58\u6a21\u578b\u53ef\u4ee5\u66f4\u597d\u5730\u62df\u5408\u6570\u636e\u3002<\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h2>\n
\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u4efb\u52a1\uff1a<\/span><\/p>\n
\u5982\u4f55\u5728 Python \u4e2d\u521b\u5efa\u6b8b\u5dee\u56fe<\/a>
\u5982\u4f55\u7528 Python \u521b\u5efa QQ \u56fe<\/a>
\u5982\u4f55\u5728 Python \u4e2d\u6d4b\u8bd5\u591a\u91cd\u5171\u7ebf\u6027<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u7ebf\u6027\u56de\u5f52\u7684\u5173\u952e\u5047\u8bbe\u4e4b\u4e00\u662f\u6b8b\u5dee\u5728\u9884\u6d4b\u53d8\u91cf\u7684\u6bcf\u4e2a\u6c34\u5e73\u4e0a\u4ee5\u76f8\u7b49\u65b9\u5dee\u5206\u5e03\u3002\u8fd9\u79cd\u5047\u8bbe\u79f0\u4e3a\u540c\u65b9\u5dee\u6027\u3002 \u5982\u679c\u4e0d\u9075\u5b88\u6b64\u5047\u8bbe\uff0c\u5219 […]<\/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-3819","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u52a0\u6743\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52 - Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u52a0\u6743\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52\uff0c\u5305\u62ec\u5206\u6b65\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\/python\u4e2d\u7684\u6700\u5c0f\u52a0\u6743\u5e73\u65b9\/\" \/>\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\u52a0\u6743\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52 - 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