{"id":1327,"date":"2023-07-26T21:04:15","date_gmt":"2023-07-26T21:04:15","guid":{"rendered":"https:\/\/statorials.org\/cn\/python-%e4%b8%ad%e7%9a%84%e5%88%86%e4%bd%8d%e6%95%b0%e5%9b%9e%e5%bd%92\/"},"modified":"2023-07-26T21:04:15","modified_gmt":"2023-07-26T21:04:15","slug":"python-%e4%b8%ad%e7%9a%84%e5%88%86%e4%bd%8d%e6%95%b0%e5%9b%9e%e5%bd%92","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/python-%e4%b8%ad%e7%9a%84%e5%88%86%e4%bd%8d%e6%95%b0%e5%9b%9e%e5%bd%92\/","title":{"rendered":"\u5982\u4f55\u5728 python \u4e2d\u6267\u884c\u5206\u4f4d\u6570\u56de\u5f52"},"content":{"rendered":"

<\/p>\n

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\u7ebf\u6027\u56de\u5f52\u662f\u4e00\u79cd\u6211\u4eec\u53ef\u4ee5\u7528\u6765\u7406\u89e3\u4e00\u4e2a\u6216\u591a\u4e2a\u9884\u6d4b\u53d8\u91cf\u4e0e\u54cd\u5e94\u53d8\u91cf<\/a>\u4e4b\u95f4\u5173\u7cfb\u7684\u65b9\u6cd5\u3002<\/span><\/p>\n

\u901a\u5e38\uff0c\u5f53\u6211\u4eec\u6267\u884c\u7ebf\u6027\u56de\u5f52\u65f6\uff0c\u6211\u4eec\u60f3\u8981\u4f30\u8ba1\u54cd\u5e94\u53d8\u91cf\u7684\u5e73\u5747\u503c\u3002<\/span><\/p>\n

\u4f46\u662f\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4e00\u79cd\u79f0\u4e3a\u5206\u4f4d\u6570\u56de\u5f52\u7684<\/strong>\u65b9\u6cd5\u6765\u4f30\u8ba1\u54cd\u5e94\u503c\u7684\u4efb\u4f55<\/em>\u5206\u4f4d\u6570\u6216\u767e\u5206\u4f4d\u6570\uff0c\u4f8b\u5982\u7b2c 70 \u4e2a\u767e\u5206\u4f4d\u6570\u3001\u7b2c 90 \u4e2a\u767e\u5206\u4f4d\u6570\u3001\u7b2c 98 \u4e2a\u767e\u5206\u4f4d\u6570\u7b49\u3002<\/span><\/p>\n

\u672c\u6559\u7a0b\u63d0\u4f9b\u4e86\u5982\u4f55\u4f7f\u7528\u6b64\u51fd\u6570\u5728 Python \u4e2d\u6267\u884c\u5206\u4f4d\u6570\u56de\u5f52\u7684\u5206\u6b65\u793a\u4f8b\u3002<\/span><\/p>\n

\u7b2c1\u6b65\uff1a\u52a0\u8f7d\u5fc5\u8981\u7684\u5305<\/strong><\/span><\/h3>\n

\u9996\u5148\uff0c\u6211\u4eec\u5c06\u52a0\u8f7d\u5fc5\u8981\u7684\u5305\u548c\u51fd\u6570\uff1a<\/span><\/p>\n

 import<\/span> numpy as<\/span> np\nimport<\/span> pandas as<\/span> pd\nimport<\/span> statsmodels. api<\/span> as<\/span> sm\nimport<\/span> statsmodels. formula<\/span> . api<\/span> as<\/span> smf\nimport<\/span> matplotlib. pyplot<\/span> as<\/span> plt\n<\/strong><\/pre>\n
\u7b2c 2 \u6b65\uff1a\u521b\u5efa\u6570\u636e<\/strong><\/span><\/h3>\n
\u5728\u6b64\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u5c06\u521b\u5efa\u4e00\u4e2a\u6570\u636e\u96c6\uff0c\u5176\u4e2d\u5305\u542b\u4e00\u6240\u5927\u5b66 100 \u540d\u5b66\u751f\u7684\u5b66\u4e60\u65f6\u95f4\u548c\u8003\u8bd5\u7ed3\u679c\uff1a<\/span><\/p>\n
 #make this example reproducible\n<\/span>n.p. random<\/span> . seeds<\/span> (0)\n\n#create dataset\n<\/span>obs = 100\n\nhours = np. random<\/span> . uniform<\/span> (1, 10, obs)\nscore = 60 + 2*hours + np. random<\/span> . normal<\/span> (loc=0, scale=.45*hours, size=100)\n\ndf = pd. DataFrame<\/span> ({' hours<\/span> ':hours, ' score<\/span> ':score})\n\n#view first five rows\n<\/span>df. head<\/span> ()\n\nhours score\n0 5.939322 68.764553\n1 7.436704 77.888040\n2 6.424870 74.196060\n3 5.903949 67.726441\n4 4.812893 72.849046<\/strong><\/pre>\n
\u6b65\u9aa4 3\uff1a\u6267\u884c\u5206\u4f4d\u6570\u56de\u5f52<\/strong><\/span><\/h3>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4f7f\u7528\u5b66\u4e60\u65f6\u95f4\u4f5c\u4e3a\u9884\u6d4b\u53d8\u91cf\u548c\u8003\u8bd5\u6210\u7ee9\u4f5c\u4e3a\u54cd\u5e94\u53d8\u91cf\u6765\u62df\u5408\u5206\u4f4d\u6570\u56de\u5f52\u6a21\u578b\u3002<\/span><\/p>\n
\u6211\u4eec\u5c06\u4f7f\u7528\u8be5\u6a21\u578b\u6839\u636e\u5b66\u4e60\u5c0f\u65f6\u6570\u6765\u9884\u6d4b\u9884\u671f\u7684 90% \u8003\u8bd5\u6210\u7ee9\uff1a<\/span><\/p>\n
 #fit the model<\/span>\nmodel = smf. quantreg<\/span> ('score~hours', df). fit<\/span> (q= 0.9<\/span> )\n\n#view model summary\n<\/span>print<\/span> ( model.summary<\/span> ())\n\n                         QuantReg Regression Results                          \n==================================================== ============================\nDept. Variable: Pseudo R-squared score: 0.6057\nModel: QuantReg Bandwidth: 3.822\nMethod: Least Squares Sparsity: 10.85\nDate: Tue, 29 Dec 2020 No. Observations: 100\nTime: 15:41:44 Df Residuals: 98\n                                        Model: 1\n==================================================== ============================\n                 coef std err t P>|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nIntercept 59.6104 0.748 79.702 0.000 58.126 61.095\nhours 2.8495 0.128 22.303 0.000 2.596 3.103\n==================================================== ============================<\/strong><\/span><\/pre>\n
\u4ece\u7ed3\u679c\u4e2d\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u4f30\u8ba1\u7684\u56de\u5f52\u65b9\u7a0b\uff1a<\/span><\/p>\n
\u8003\u8bd5\u6210\u7ee9\u7684\u7b2c 90 \u4e2a\u767e\u5206\u4f4d\u6570 = 59.6104 + 2.8495*\uff08\u5c0f\u65f6\uff09<\/span><\/p>\n
\u4f8b\u5982\uff0c\u6240\u6709\u5b66\u4e60 8 \u5c0f\u65f6\u7684\u5b66\u751f\u7684\u7b2c 90 \u4e2a\u767e\u5206\u4f4d\u6570\u5206\u6570\u5e94\u4e3a 82.4\uff1a<\/span><\/p>\n
\u8003\u8bd5\u6210\u7ee9\u7684\u7b2c 90 \u4e2a\u767e\u5206\u4f4d\u6570 = 59.6104 + 2.8495*(8) = 82.4<\/strong> \u3002<\/span><\/p>\n
\u8f93\u51fa\u8fd8\u663e\u793a\u9884\u6d4b\u53d8\u91cf\u7684\u622a\u8ddd\u548c\u65f6\u95f4\u7684\u7f6e\u4fe1\u4e0a\u9650\u548c\u4e0b\u9650\u3002<\/span><\/p>\n
\u7b2c 4 \u6b65\uff1a\u53ef\u89c6\u5316\u7ed3\u679c<\/strong><\/span><\/h3>\n
\u6211\u4eec\u8fd8\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u6563\u70b9\u56fe\u6765\u53ef\u89c6\u5316\u56de\u5f52\u7ed3\u679c\uff0c\u5e76\u5c06\u62df\u5408\u7684\u5206\u4f4d\u6570\u56de\u5f52\u65b9\u7a0b\u53e0\u52a0\u5728\u56fe\u8868\u4e0a\uff1a<\/span> <\/p>\n
 #define figure and axis\n<\/span>fig, ax = plt.subplots(figsize=(8, 6))\n\n#get y values\n<\/span>get_y = lambda<\/span> a, b: a + b * hours\ny = get_y( model.params<\/span> [' Intercept<\/span> '], model.params<\/span> [' hours<\/span> '])\n\n#plot data points with quantile regression equation overlaid\n<\/span>ax. plot<\/span> (hours, y, color=' black<\/span> ')\nax. scatter<\/span> (hours, score, alpha=.3)\nax. set_xlabel<\/span> (' Hours Studied<\/span> ', fontsize=14)\nax. set_ylabel<\/span> (' Exam Score<\/span> ', fontsize=14)\n<\/strong><\/span><\/pre>\n
\"Python<\/p>\n
\u4e0e\u7b80\u5355\u7684\u7ebf\u6027\u56de\u5f52\u7ebf\u4e0d\u540c\uff0c\u8bf7\u6ce8\u610f\uff0c\u8fd9\u6761\u62df\u5408\u7ebf\u5e76\u4e0d\u4ee3\u8868\u6570\u636e\u7684\u201c\u6700\u4f73\u62df\u5408\u7ebf\u201d\u3002\u76f8\u53cd\uff0c\u5b83\u4f1a\u901a\u8fc7\u9884\u6d4b\u53d8\u91cf\u6bcf\u4e2a\u6c34\u5e73\u7684\u4f30\u8ba1\u7b2c 90 \u4e2a\u767e\u5206\u4f4d\u3002<\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n
\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u7b80\u5355\u7ebf\u6027\u56de\u5f52<\/a>
\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u4e8c\u6b21\u56de\u5f52<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u7ebf\u6027\u56de\u5f52\u662f\u4e00\u79cd\u6211\u4eec\u53ef\u4ee5\u7528\u6765\u7406\u89e3\u4e00\u4e2a\u6216\u591a\u4e2a\u9884\u6d4b\u53d8\u91cf\u4e0e\u54cd\u5e94\u53d8\u91cf\u4e4b\u95f4\u5173\u7cfb\u7684\u65b9\u6cd5\u3002 \u901a\u5e38\uff0c\u5f53\u6211\u4eec\u6267\u884c\u7ebf\u6027\u56de\u5f52\u65f6\uff0c\u6211\u4eec\u60f3 […]<\/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-1327","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u5206\u4f4d\u6570\u56de\u5f52 - Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u5206\u4f4d\u6570\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\u5206\u4f4d\u6570\u56de\u5f52\/\" \/>\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\u5206\u4f4d\u6570\u56de\u5f52 - 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