{"id":828,"date":"2023-07-28T15:16:06","date_gmt":"2023-07-28T15:16:06","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e5%81%8f%e7%9b%b8%e5%85%b3python\/"},"modified":"2023-07-28T15:16:06","modified_gmt":"2023-07-28T15:16:06","slug":"%e5%81%8f%e7%9b%b8%e5%85%b3python","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e5%81%8f%e7%9b%b8%e5%85%b3python\/","title":{"rendered":"\u5982\u4f55\u5728python\u4e2d\u8ba1\u7b97\u504f\u76f8\u5173"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u5728\u7edf\u8ba1\u5b66\u4e2d\uff0c\u6211\u4eec\u7ecf\u5e38\u4f7f\u7528<\/span><a href=\"https:\/\/statorials.org\/cn\/\u76ae\u5c14\u900a\u76f8\u5173\u7cfb\u6570-1\/\" target=\"_blank\" rel=\"noopener\">\u76ae\u5c14\u900a\u76f8\u5173\u7cfb\u6570<\/a><span style=\"color: #000000;\">\u6765\u8861\u91cf\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u7ebf\u6027\u5173\u7cfb\u3002\u7136\u800c\uff0c\u6709\u65f6\u6211\u4eec\u60f3<strong>\u5728\u63a7\u5236\u7b2c\u4e09\u4e2a\u53d8\u91cf\u7684\u540c\u65f6<\/strong>\u4e86\u89e3\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4f8b\u5982\uff0c\u5047\u8bbe\u6211\u4eec\u60f3\u8981\u8861\u91cf\u5b66\u751f\u7684\u5b66\u4e60\u65f6\u6570\u4e0e\u671f\u672b\u8003\u8bd5\u6210\u7ee9\u4e4b\u95f4\u7684\u5173\u8054\uff0c\u540c\u65f6\u63a7\u5236\u5b66\u751f\u5728\u73ed\u7ea7\u4e2d\u7684\u5f53\u524d\u6210\u7ee9\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>\u504f\u76f8\u5173<\/strong>\u6765\u8861\u91cf\u5b66\u4e60\u65f6\u95f4\u548c\u671f\u672b\u8003\u8bd5\u6210\u7ee9\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u5728 Python \u4e2d\u8ba1\u7b97\u504f\u76f8\u5173\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b\uff1aPython \u4e2d\u7684\u90e8\u5206\u76f8\u5173<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b Pandas DataFrame\uff0c\u5b83\u663e\u793a 10 \u540d\u5b66\u751f\u7684\u5f53\u524d\u6210\u7ee9\u3001\u603b\u5b66\u4e60\u65f6\u95f4\u548c\u671f\u672b\u8003\u8bd5\u6210\u7ee9\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> numpy <span style=\"color: #107d3f;\">as<\/span> np\n<span style=\"color: #107d3f;\">import<\/span> panda <span style=\"color: #107d3f;\">as<\/span> pd\n\ndata = {'currentGrade': [82, 88, 75, 74, 93, 97, 83, 90, 90, 80],\n        'hours': [4, 3, 6, 5, 4, 5, 8, 7, 4, 6],\n        'examScore': [88, 85, 76, 70, 92, 94, 89, 85, 90, 93],\n        }\n\ndf = pd.DataFrame(data, columns = ['currentGrade','hours', 'examScore'])\ndf\n\n   currentGrade hours examScore\n0 82 4 88\n1 88 3 85\n2 75 6 76\n3 74 5 70\n4 93 4 92\n5 97 5 94\n6 83 8 89\n7 90 7 85\n8 90 4 90\n9 80 6 93\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u8981\u5728\u63a7\u5236<strong>currentGrade<\/strong>\u7684\u540c\u65f6\u8ba1\u7b97<strong>\u5c0f\u65f6\u6570<\/strong>\u548c<strong>examScore<\/strong>\u4e4b\u95f4\u7684\u504f\u76f8\u5173\u6027\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<a href=\"https:\/\/pingouin-stats.org\/index.html\" target=\"_blank\" rel=\"noopener\">penguin \u5305<\/a>\u4e2d\u7684<strong>partial_corr()<\/strong>\u51fd\u6570\uff0c\u8be5\u51fd\u6570\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\uff1a<\/span><\/p>\n<p style=\"text-align: center;\"> <strong><span style=\"color: #000000;\">partial_corr\uff08\u6570\u636e\uff0cx\uff0cy\uff0c\u534f\u65b9\u5dee\uff09<\/span><\/strong><\/p>\n<p><span style=\"color: #000000;\">\u91d1\u5b50\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>data\uff1a<\/strong>\u6570\u636e\u6846\u7684\u540d\u79f0<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>x, y\uff1a<\/strong>\u6570\u636e\u6846\u4e2d\u7684\u5217\u540d\u79f0<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>covar\uff1a<\/strong>\u6570\u636e\u6846\u4e2d\u534f\u53d8\u91cf\u5217\u7684\u540d\u79f0\uff08\u4f8b\u5982\u60a8\u6b63\u5728\u63a7\u5236\u7684\u53d8\u91cf\uff09<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u662f\u5728\u6b64\u7279\u5b9a\u793a\u4f8b\u4e2d\u4f7f\u7528\u6b64\u51fd\u6570\u7684\u65b9\u6cd5\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#install and import penguin package<\/span> \npip <span style=\"color: #107d3f;\">install<\/span> penguin\n<span style=\"color: #107d3f;\">import<\/span> penguin <span style=\"color: #107d3f;\">as<\/span> pg\n\n<span style=\"color: #008080;\">#find partial correlation between hours and exam score while controlling for grade<\/span>\npg.partial_corr(data=df, x='hours', y='examScore', covar='currentGrade')\n\n\n         n r CI95% r2 adj_r2 p-val BF10 power\npearson 10 0.191 [-0.5, 0.73] 0.036 -0.238 0.598 0.438 0.082\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0c\u5b66\u4e60\u65f6\u95f4\u548c\u671f\u672b\u8003\u8bd5\u6210\u7ee9\u4e4b\u95f4\u7684\u504f\u76f8\u5173\u4e3a<strong>0.191<\/strong> \uff0c\u5c5e\u4e8e\u8f83\u5c0f\u7684\u6b63\u76f8\u5173\u3002\u968f\u7740\u5b66\u4e60\u65f6\u95f4\u7684\u589e\u52a0\uff0c\u5047\u8bbe\u5f53\u524d\u6210\u7ee9\u4fdd\u6301\u4e0d\u53d8\uff0c\u8003\u8bd5\u6210\u7ee9\u4e5f\u5f80\u5f80\u4f1a\u589e\u52a0\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8981\u540c\u65f6\u8ba1\u7b97\u591a\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u504f\u76f8\u5173\u6027\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>.pcorr()<\/strong>\u51fd\u6570\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate all pairwise partial correlations, rounded to three decimal places<\/span>\ndf.pcorr().round(3)\n\n\t     currentGrade hours examScore\ncurrentGrade 1.000 -0.311 0.736\nhours -0.311 1.000 0.191\nexamScore 0.736 0.191 1.000\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u7ed3\u679c\u89e3\u91ca\u65b9\u5f0f\u5982\u4e0b\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u5f53\u524d\u6210\u7ee9\u4e0e\u5b66\u4e60\u65f6\u95f4\u4e4b\u95f4\u7684\u504f\u76f8\u5173\u4e3a<strong>-0.311<\/strong> \u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5f53\u524d\u6210\u7ee9\u4e0e\u8003\u8bd5\u6210\u7ee9\u4e4b\u95f4\u7684\u504f\u76f8\u5173\u4e3a<strong>0.736<\/strong> \u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5b66\u4e60\u65f6\u95f4\u548c\u8003\u8bd5\u6210\u7ee9\u4e4b\u95f4\u7684\u504f\u76f8\u5173\u4e3a<strong>0.191<\/strong> \u3002<\/span><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>\u5728\u7edf\u8ba1\u5b66\u4e2d\uff0c\u6211\u4eec\u7ecf\u5e38\u4f7f\u7528\u76ae\u5c14\u900a\u76f8\u5173\u7cfb\u6570\u6765\u8861\u91cf\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u7ebf\u6027\u5173\u7cfb\u3002\u7136\u800c\uff0c\u6709\u65f6\u6211\u4eec\u60f3\u5728\u63a7\u5236\u7b2c\u4e09\u4e2a\u53d8\u91cf\u7684\u540c\u65f6\u4e86\u89e3 [&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-828","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\u8ba1\u7b97\u504f\u76f8\u5173 - \u7edf\u8ba1\u5b66<\/title>\n<meta name=\"description\" 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