{"id":3027,"date":"2023-07-19T14:05:33","date_gmt":"2023-07-19T14:05:33","guid":{"rendered":"https:\/\/statorials.org\/cn\/python%e4%b8%ad%e7%9a%84%e7%9b%b8%e5%85%b3%e6%80%a7%e6%b5%8b%e8%af%95\/"},"modified":"2023-07-19T14:05:33","modified_gmt":"2023-07-19T14:05:33","slug":"python%e4%b8%ad%e7%9a%84%e7%9b%b8%e5%85%b3%e6%80%a7%e6%b5%8b%e8%af%95","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/python%e4%b8%ad%e7%9a%84%e7%9b%b8%e5%85%b3%e6%80%a7%e6%b5%8b%e8%af%95\/","title":{"rendered":"\u5982\u4f55\u5728 python \u4e2d\u6267\u884c\u76f8\u5173\u6027\u6d4b\u8bd5\uff08\u5e26\u6709\u793a\u4f8b\uff09"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u91cf\u5316\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u5173\u7cfb\u7684\u4e00\u79cd\u65b9\u6cd5\u662f\u4f7f\u7528<a href=\"https:\/\/statorials.org\/cn\/\u76ae\u5c14\u900a\u76f8\u5173\u7cfb\u6570-1\/\" target=\"_blank\" rel=\"noopener noreferrer\">Pearson \u76f8\u5173\u7cfb\u6570<\/a>\uff0c\u5b83\u8861\u91cf\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u7ebf\u6027\u5173\u8054<em>\u3002<\/em><\/span><\/p>\n<p><span style=\"color: #000000;\">\u5b83\u59cb\u7ec8\u91c7\u7528 -1 \u548c 1 \u4e4b\u95f4\u7684\u503c\uff0c\u5176\u4e2d\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>-1<\/strong>\u8868\u793a\u5b8c\u5168\u8d1f\u7ebf\u6027\u76f8\u5173<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>0<\/strong>\u8868\u793a\u6ca1\u6709\u7ebf\u6027\u76f8\u5173<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>1<\/strong>\u8868\u793a\u5b8c\u5168\u6b63\u7ebf\u6027\u76f8\u5173<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u8981\u786e\u5b9a\u76f8\u5173\u7cfb\u6570\u662f\u5426\u5177\u6709\u7edf\u8ba1\u663e\u7740\u6027\uff0c\u60a8\u53ef\u4ee5\u8ba1\u7b97\u76f8\u5e94\u7684 t \u5206\u6570\u548c p \u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u76f8\u5173\u7cfb\u6570 (r) \u7684 t \u5206\u6570\u8ba1\u7b97\u516c\u5f0f\u4e3a\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>t<\/strong> = r * \u221a <span style=\"border-top: 1px solid black;\">n-2<\/span> \/ \u221a <span style=\"border-top: 1px solid black;\">1-r <sup>2<\/sup><\/span><\/span><\/p>\n<p><span style=\"color: #000000;\">\u7136\u540e\u5c06 p \u503c\u8ba1\u7b97\u4e3a\u5177\u6709 n-2 \u81ea\u7531\u5ea6\u7684 t \u5206\u5e03\u7684\u76f8\u5e94\u53cc\u5c3e p \u503c\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b\uff1aPython \u4e2d\u7684\u76f8\u5173\u6027\u6d4b\u8bd5<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u8981\u786e\u5b9a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u7cfb\u6570\u662f\u5426\u5177\u6709\u7edf\u8ba1\u663e\u7740\u6027\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528<strong>SciPy<\/strong>\u5e93\u4e2d\u7684<strong>pearsonr<\/strong>\u51fd\u6570\u5728 Python \u4e2d\u6267\u884c\u76f8\u5173\u6027\u6d4b\u8bd5\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b64\u51fd\u6570\u8fd4\u56de\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u7cfb\u6570\u4ee5\u53ca\u53cc\u5c3e p \u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4f8b\u5982\uff0c\u5047\u8bbe\u6211\u4eec\u5728Python\u4e2d\u6709\u4ee5\u4e0b\u4e24\u4e2a\u8868\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create two arrays\n<\/span>x = [3, 4, 4, 5, 7, 8, 10, 12, 13, 15]\ny = [2, 4, 4, 5, 4, 7, 8, 19, 14, 10]\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u5bfc\u5165<strong>pearsonr<\/strong>\u51fd\u6570\uff0c\u8ba1\u7b97\u4e24\u4e2a\u8868\u4e4b\u95f4\u7684Pearson\u76f8\u5173\u7cfb\u6570\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> scipy. <span style=\"color: #3366ff;\">stats<\/span> . <span style=\"color: #3366ff;\">stats<\/span> <span style=\"color: #008000;\">import<\/span> pearsonr\n\n<span style=\"color: #008080;\">#calculation correlation coefficient and p-value between x and y<\/span>\npearsonr(x, y)\n\n(0.8076177030748631, 0.004717255828132089)\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u662f\u5982\u4f55\u89e3\u91ca\u7ed3\u679c\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u76ae\u5c14\u900a\u76f8\u5173\u7cfb\u6570\uff08r\uff09\uff1a <strong>0.8076<\/strong><\/span><\/li>\n<li><span style=\"color: #000000;\">\u4e24\u4fa7 p \u503c\uff1a <strong>0.0047<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u76f8\u5173\u7cfb\u6570\u63a5\u8fd1 1\uff0c\u8fd9\u544a\u8bc9\u6211\u4eec\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u5b58\u5728\u5f88\u5f3a\u7684\u6b63\u76f8\u5173\u5173\u7cfb\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7531\u4e8e\u76f8\u5e94\u7684 p \u503c\u5c0f\u4e8e 0.05\uff0c\u6211\u4eec\u5f97\u51fa\u7ed3\u8bba\uff0c\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u5b58\u5728\u7edf\u8ba1\u4e0a\u663e\u7740\u7684\u5173\u8054\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u4ece<strong>\u76ae\u5c14\u900a<\/strong>\u51fd\u6570\u4e2d\u63d0\u53d6\u4e2a\u4f53\u76f8\u5173\u7cfb\u6570\u548c p \u503c\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#extract correlation coefficient (rounded to 4 decimal places)<\/span>\nr = round(pearsonr(x, y)[ <span style=\"color: #008000;\">0<\/span> ], 4)\n\n<span style=\"color: #008000;\">print<\/span> (r)\n\n0.8076\n\n<span style=\"color: #008080;\">#extract p-value (rounded to 4 decimal places)<\/span> \np = round(pearsonr(x, y)[ <span style=\"color: #008000;\">1<\/span> ], 4)\n\n<span style=\"color: #008000;\">print<\/span> (p) \n\n0.0047\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4e0e\u539f\u59cb<strong>pearsonr<\/strong>\u51fd\u6570\u7684\u8f93\u51fa\u76f8\u6bd4\uff0c\u8fd9\u4e9b\u503c\u66f4\u5bb9\u6613\u9605\u8bfb\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u6559\u7a0b\u63d0\u4f9b\u6709\u5173\u76f8\u5173\u7cfb\u6570\u7684\u5176\u4ed6\u4fe1\u606f\uff1a<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/cn\/\u76ae\u5c14\u900a\u76f8\u5173\u7cfb\u6570-1\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u76ae\u5c14\u900a\u76f8\u5173\u7cfb\u6570\u7b80\u4ecb<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\" target=\"_blank\" rel=\"noopener\">\u4ec0\u4e48\u88ab\u8ba4\u4e3a\u662f\u201c\u5f3a\u201d\u76f8\u5173\u6027\uff1f<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u76ae\u5c14\u900a\u76f8\u5173\u5047\u8bbe\/\" target=\"_blank\" rel=\"noopener\">\u76ae\u5c14\u900a\u76f8\u5173\u6027\u7684\u4e94\u4e2a\u5047\u8bbe<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u91cf\u5316\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u5173\u7cfb\u7684\u4e00\u79cd\u65b9\u6cd5\u662f\u4f7f\u7528Pearson \u76f8\u5173\u7cfb\u6570\uff0c\u5b83\u8861\u91cf\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u7ebf\u6027\u5173\u8054\u3002 \u5b83\u59cb\u7ec8\u91c7\u7528 -1 [&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-3027","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\u6267\u884c\u76f8\u5173\u6027\u6d4b\u8bd5\uff08\u5e26\u6709\u793a\u4f8b\uff09 - 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