{"id":496,"date":"2023-07-29T17:26:06","date_gmt":"2023-07-29T17:26:06","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e9%b1%bc%e5%9b%9e%e5%bd%92\/"},"modified":"2023-07-29T17:26:06","modified_gmt":"2023-07-29T17:26:06","slug":"%e9%b1%bc%e5%9b%9e%e5%bd%92","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e9%b1%bc%e5%9b%9e%e5%bd%92\/","title":{"rendered":"\u8ba1\u6570\u6570\u636e\u6cca\u677e\u56de\u5f52\u7684\u7b80\u8981\u4ecb\u7ecd"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>\u56de\u5f52<\/strong>\u662f\u4e00\u79cd\u7edf\u8ba1\u65b9\u6cd5\uff0c\u53ef\u7528\u4e8e\u786e\u5b9a\u4e00\u4e2a\u6216\u591a\u4e2a\u9884\u6d4b\u53d8\u91cf\u4e0e<a href=\"https:\/\/statorials.org\/cn\/\u53d8\u91cf\u89e3\u91ca\u6027\u53cd\u5e94\/\" target=\"_blank\" rel=\"noopener\">\u54cd\u5e94\u53d8\u91cf<\/a>\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6cca\u677e\u56de\u5f52<\/strong>\u662f\u4e00\u79cd\u7279\u6b8a\u7c7b\u578b\u7684\u56de\u5f52\uff0c\u5176\u4e2d\u54cd\u5e94\u53d8\u91cf\u662f\u201c\u8ba1\u6570\u6570\u636e\u201d\u3002\u4ee5\u4e0b\u793a\u4f8b\u8bf4\u660e\u4e86\u53ef\u4ee5\u4f7f\u7528\u6cca\u677e\u56de\u5f52\u7684\u60c5\u51b5\uff1a<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u793a\u4f8b 1\uff1a<\/strong>\u6cca\u677e\u56de\u5f52\u53ef\u7528\u4e8e\u6839\u636e\u8fdb\u5165\u7279\u5b9a\u5927\u5b66\u8bfe\u7a0b\u65f6\u7684 GPA \u548c\u6027\u522b\u6765\u68c0\u67e5\u4ece\u7279\u5b9a\u5927\u5b66\u8bfe\u7a0b\u6bd5\u4e1a\u7684\u5b66\u751f\u4eba\u6570\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u201c\u6bd5\u4e1a\u5b66\u751f\u4eba\u6570\u201d\u662f\u54cd\u5e94\u53d8\u91cf\uff0c\u201c\u8bfe\u7a0b\u5165\u5b66\u65f6\u7684 GPA\u201d\u662f\u8fde\u7eed\u9884\u6d4b\u53d8\u91cf\uff0c\u201c\u6027\u522b\u201d\u662f\u5206\u7c7b\u9884\u6d4b\u53d8\u91cf\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u793a\u4f8b 2\uff1a<\/strong>\u6cca\u677e\u56de\u5f52\u53ef\u7528\u4e8e\u6839\u636e\u5929\u6c14\u6761\u4ef6\uff08\u201c\u6674\u5929\u201d\u3001\u201c\u9634\u5929\u201d\u3001\u201c\u96e8\u5929\u201d\uff09\u4ee5\u53ca\u57ce\u5e02\u4e2d\u662f\u5426\u53d1\u751f\u7279\u6b8a\u4e8b\u4ef6\uff08\u201c\u662f\u201d\uff09\u6765\u68c0\u67e5\u7279\u5b9a\u8def\u53e3\u7684\u4ea4\u901a\u4e8b\u6545\u6570\u91cf\u6216\u6ca1\u6709\u201d\uff09\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u201c\u9053\u8def\u4e8b\u6545\u6570\u91cf\u201d\u662f\u54cd\u5e94\u53d8\u91cf\uff0c\u800c\u201c\u5929\u6c14\u72b6\u51b5\u201d\u548c\u201c\u7279\u6b8a\u4e8b\u4ef6\u201d\u90fd\u662f\u5206\u7c7b\u9884\u6d4b\u53d8\u91cf\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u793a\u4f8b 3\uff1a<\/strong>\u6cca\u677e\u56de\u5f52\u53ef\u7528\u4e8e\u6839\u636e\u4e00\u5929\u4e2d\u7684\u65f6\u95f4\u3001\u4e00\u5468\u4e2d\u7684\u67d0\u4e00\u5929\u4ee5\u53ca\u662f\u5426\u6b63\u5728\u8fdb\u884c\u4fc3\u9500\uff08\u201c\u662f\u6216\u5426\u201d\uff09\u6765\u68c0\u67e5\u5546\u5e97\u4e2d\u60a8\u524d\u9762\u6392\u961f\u7684\u4eba\u6570\u201d\uff09\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u201c\u6392\u5728\u4f60\u524d\u9762\u7684\u4eba\u6570\u201d\u662f\u54cd\u5e94\u53d8\u91cf\uff0c\u201c\u4e00\u5929\u4e2d\u7684\u65f6\u95f4\u201d\u548c\u201c\u4e00\u5468\u4e2d\u7684\u67d0\u4e00\u5929\u201d\u90fd\u662f\u8fde\u7eed\u9884\u6d4b\u53d8\u91cf\uff0c\u201c\u6b63\u5728\u8fdb\u884c\u7684\u9500\u552e\u201d\u662f\u5206\u7c7b\u9884\u6d4b\u53d8\u91cf\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u793a\u4f8b 4\uff1a<\/strong>\u6cca\u677e\u56de\u5f52\u53ef\u7528\u4e8e\u6839\u636e\u5929\u6c14\u6761\u4ef6\uff08\u201c\u6674\u5929\u201d\u3001\u201c\u9634\u5929\u201d\u3001\u201c\u96e8\u5929\u201d\uff09\u548c\u8def\u7ebf\u96be\u5ea6\uff08\u201c\u8f7b\u677e\u201d\u3001\u201c\u96e8\u5929\u201d\uff09\u68c0\u67e5\u5b8c\u6210\u94c1\u4eba\u4e09\u9879\u7684\u4eba\u6570\u3002\u4e2d\u7b49\u201d\u3001\u201c\u56f0\u96be\u201d\uff09\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u201c\u5b8c\u6210\u6bd4\u8d5b\u7684\u4eba\u6570\u201d\u662f\u54cd\u5e94\u53d8\u91cf\uff0c\u800c\u201c\u5929\u6c14\u6761\u4ef6\u201d\u548c\u201c\u8bfe\u7a0b\u96be\u5ea6\u201d\u90fd\u662f\u5206\u7c7b\u9884\u6d4b\u53d8\u91cf\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6267\u884c\u6cca\u677e\u56de\u5f52\u5c06\u5141\u8bb8\u60a8\u67e5\u770b\u54ea\u4e9b\u9884\u6d4b\u53d8\u91cf\uff08\u5982\u679c\u6709\uff09\u5bf9\u54cd\u5e94\u53d8\u91cf\u5177\u6709\u7edf\u8ba1\u663e\u7740\u5f71\u54cd\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5bf9\u4e8e\u8fde\u7eed\u9884\u6d4b\u53d8\u91cf\uff0c\u60a8\u5c06\u80fd\u591f\u89e3\u91ca\u8be5\u53d8\u91cf\u7684 1 \u4e2a\u5355\u4f4d\u7684\u589e\u52a0\u6216\u51cf\u5c11\u5982\u4f55\u4e0e\u54cd\u5e94\u53d8\u91cf\u6570\u5b57\u7684\u767e\u5206\u6bd4\u53d8\u5316\u76f8\u5173\u8054\uff08\u4f8b\u5982\uff0c\u201cGPA \u6bcf\u589e\u52a0 1 \u4e2a\u5355\u4f4d\u4e0e\u54cd\u5e94\u53d8\u91cf\u589e\u52a0 12.5%\uff09\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5bf9\u4e8e\u5206\u7c7b\u9884\u6d4b\u53d8\u91cf\uff0c\u60a8\u5c06\u80fd\u591f\u89e3\u91ca\u4e00\u7ec4\u8ba1\u6570\uff08\u4f8b\u5982\uff0c\u5728\u6674\u5929\u5b8c\u6210\u94c1\u4eba\u4e09\u9879\u7684\u4eba\u6570\uff09\u4e0e\u53e6\u4e00\u7ec4\uff08\u4f8b\u5982\uff0c\u5b8c\u6210\u4e00\u9879\u94c1\u4eba\u4e09\u9879\u7684\u4eba\u6570\uff09\u76f8\u6bd4\u7684\u767e\u5206\u6bd4\u53d8\u5316\u3002\u96e8\u5929\u53c2\u52a0\u94c1\u4eba\u4e09\u9879\uff09\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u6cca\u677e\u56de\u5f52\u7684\u5047\u8bbe<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u5728\u6267\u884c\u6cca\u677e\u56de\u5f52\u4e4b\u524d\uff0c\u6211\u4eec\u5fc5\u987b\u786e\u4fdd\u6ee1\u8db3\u4ee5\u4e0b\u5047\u8bbe\uff0c\u4ee5\u4fbf\u6211\u4eec\u7684\u6cca\u677e\u56de\u5f52\u7ed3\u679c\u6709\u6548\uff1a<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u5047\u8bbe1\uff1a<\/strong><strong>\u54cd\u5e94\u53d8\u91cf\u662f\u8ba1\u6570\u6570\u636e\u3002<\/strong>\u5728\u4f20\u7edf\u7684\u7ebf\u6027\u56de\u5f52\u4e2d\uff0c\u54cd\u5e94\u53d8\u91cf\u662f\u8fde\u7eed\u6570\u636e\u3002\u7136\u800c\uff0c\u8981\u4f7f\u7528\u6cca\u677e\u56de\u5f52\uff0c\u6211\u4eec\u7684\u54cd\u5e94\u53d8\u91cf\u5fc5\u987b\u7531\u8ba1\u6570\u6570\u636e\u7ec4\u6210\uff0c\u5176\u4e2d\u5305\u62ec0 \u6216\u66f4\u5927\u7684\u6574\u6570\uff08\u4f8b\u59820\u30011\u30012\u300114\u300134\u300149\u3001200 \u7b49\uff09\u3002\u6211\u4eec\u7684\u54cd\u5e94\u53d8\u91cf\u4e0d\u80fd\u5305\u542b\u8d1f\u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u5047\u8bbe2\uff1a\u89c2\u5bdf\u7ed3\u679c\u662f\u72ec\u7acb\u7684\u3002<\/strong>\u6570\u636e\u96c6\u4e2d\u7684\u6bcf\u4e2a<a href=\"https:\/\/statorials.org\/cn\/\u7edf\u8ba1\u89c2\u5bdf\/\" target=\"_blank\" rel=\"noopener\">\u89c2\u6d4b\u503c<\/a>\u5fc5\u987b\u5f7c\u6b64\u72ec\u7acb\u3002\u8fd9\u610f\u5473\u7740\u4e00\u4e2a\u89c2\u5bdf\u7ed3\u679c\u4e0d\u5e94\u8be5\u80fd\u591f\u63d0\u4f9b\u6709\u5173\u53e6\u4e00\u89c2\u5bdf\u7ed3\u679c\u7684\u4fe1\u606f\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u5047\u8bbe3\uff1a\u8d26\u6237\u5206\u5e03\u670d\u4ece\u6cca\u677e\u5206\u5e03\u3002<\/strong>\u56e0\u6b64\uff0c\u89c2\u5bdf\u5230\u7684\u8ba1\u6570\u548c\u9884\u671f\u8ba1\u6570\u5e94\u8be5\u76f8\u4f3c\u3002\u6d4b\u8bd5\u8fd9\u4e00\u70b9\u7684\u4e00\u4e2a\u7b80\u5355\u65b9\u6cd5\u662f\u7ed8\u5236\u9884\u671f\u8ba1\u6570\u548c\u89c2\u5bdf\u5230\u7684\u8ba1\u6570\u5e76\u67e5\u770b\u5b83\u4eec\u662f\u5426\u76f8\u4f3c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u5047\u8bbe 4\uff1a\u6a21\u578b\u7684\u5747\u503c\u548c\u65b9\u5dee\u76f8\u7b49\u3002<\/strong>\u8fd9\u662f\u57fa\u4e8e\u8ba1\u6570\u5206\u5e03\u9075\u5faa\u6cca\u677e\u5206\u5e03\u7684\u5047\u8bbe\u3002\u5bf9\u4e8e\u6cca\u677e\u5206\u5e03\uff0c\u65b9\u5dee\u4e0e\u5747\u503c\u5177\u6709\u76f8\u540c\u7684\u503c\u3002\u5982\u679c\u6ee1\u8db3\u8fd9\u4e2a\u5047\u8bbe\uff0c\u90a3\u4e48\u5c31\u5177\u6709<strong>\u7b49\u5206\u6563\u6027<\/strong>\u3002\u7136\u800c\uff0c\u8fd9\u4e00\u5047\u8bbe\u7ecf\u5e38\u88ab\u8fdd\u53cd\uff0c\u56e0\u4e3a\u8fc7\u5ea6\u5206\u6563\u662f\u4e00\u4e2a\u5e38\u89c1\u95ee\u9898\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b\uff1aR \u4e2d\u7684\u6cca\u677e\u56de\u5f52<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u73b0\u5728\u6211\u4eec\u5c06\u56de\u987e\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u6cca\u677e\u56de\u5f52\u7684\u793a\u4f8b\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u80cc\u666f<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u6211\u4eec\u60f3\u77e5\u9053\u7ed9\u5b9a\u53bf\u7684\u4e00\u540d\u9ad8\u4e2d\u68d2\u7403\u8fd0\u52a8\u5458\u6839\u636e\u4ed6\u7684\u5b66\u533a\uff08\u201cA\u201d\u3001\u201cB\u201d\u6216\u201cC\u201d\uff09\u548c\u4ed6\u7684\u5b66\u6821\u6210\u7ee9\u83b7\u5f97\u4e86\u591a\u5c11\u5956\u5b66\u91d1\u3002\u5927\u5b66\u5165\u5b66\u8003\u8bd5\uff08\u4ece0\u5230100\u6d4b\u91cf\uff09\u3002 \uff09\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u521b\u5efa\u6211\u4eec\u5c06\u4f7f\u7528\u7684\u6570\u636e\u96c6\uff0c\u5176\u4e2d\u5305\u62ec 100 \u540d\u68d2\u7403\u8fd0\u52a8\u5458\u7684\u6570\u636e\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#make this example reproducible\n<\/span>set.seed(1)\n\n<span style=\"color: #008080;\">#create dataset\n<\/span>data &lt;- data.frame(offers = c(rep(0, 50), rep(1, 30), rep(2, 10), rep(3, 7), rep(4, 3)),\n                   division = sample(c(\"A\", \"B\", \"C\"), 100, replace = TRUE),\n                   exam = c(runif(50, 60, 80), runif(30, 65, 95), runif(20, 75, 95)))<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u4e86\u89e3\u6570\u636e<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u5728\u5b9e\u9645\u5c06\u6cca\u677e\u56de\u5f52\u6a21\u578b\u62df\u5408\u5230\u8be5\u6570\u636e\u96c6\u4e4b\u524d\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u53ef\u89c6\u5316\u6570\u636e\u96c6\u7684\u524d\u51e0\u884c\u5e76\u4f7f\u7528<strong><a href=\"https:\/\/dplyr.tidyverse.org\/\" target=\"_blank\" rel=\"noopener\">dplyr<\/a><\/strong>\u5e93\u8fd0\u884c\u6c47\u603b\u7edf\u8ba1\u6765\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view dimensions of dataset<\/span>\ndim(data)\n\n#[1] 100 3\n\n<span style=\"color: #008080;\">#view first six lines of dataset<\/span>\nhead(data)\n\n# offers division exam\n#1 0 A 73.09448\n#2 0 B 67.06395\n#3 0 B 65.40520\n#4 0 C 79.85368\n#5 0 A 72.66987\n#6 0 C 64.26416\n\n<span style=\"color: #008080;\">#view summary of each variable in dataset<\/span>\nsummary(data)\n\n# offers division exam      \n# Min. :0.00 To:27 Min. :60.26  \n# 1st Qu.:0.00 B:38 1st Qu.:69.86  \n# Median: 0.50 C:35 Median: 75.08  \n# Mean:0.83 Mean:76.43  \n# 3rd Qu.:1.00 3rd Qu.:82.87  \n# Max. :4.00 Max. :93.87  \n\n<span style=\"color: #008080;\">#view mean exam score by number of offers<\/span>\nlibrary(dplyr)\ndata %&gt;%\n  <span style=\"color: #800080;\">group_by<\/span> (offers) %&gt;%\n  <span style=\"color: #800080;\">summarize<\/span> (mean_exam = mean(exam))\n\n# A tibble: 5 x 2\n# offers mean_exam\n#        \n#1 0 70.0\n#2 1 80.8\n#3 2 86.8\n#4 3 83.9\n#5 4 87.9<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ece\u4e0a\u9762\u7684\u7ed3\u679c\u6211\u4eec\u53ef\u4ee5\u89c2\u5bdf\u5230\u4ee5\u4e0b\u51e0\u70b9\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u6570\u636e\u96c6\u4e2d\u6709 100 \u884c\u548c 3 \u5217<\/span><\/li>\n<li><span style=\"color: #000000;\">\u4e00\u540d\u7403\u5458\u6536\u5230\u7684\u62a5\u4ef7\u6700\u5c11\u4e3a\u96f6\uff0c\u6700\u591a\u4e3a\u56db\uff0c\u5e73\u5747\u4e3a 0.83\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5728\u6b64\u6570\u636e\u96c6\u4e2d\uff0c\u201cA\u201d\u7ec4\u6709 27 \u540d\u7403\u5458\uff0c\u201cB\u201d\u7ec4\u6709 38 \u540d\u7403\u5458\uff0c\u201cC\u201d\u7ec4\u6709 35 \u540d\u7403\u5458\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u8003\u8bd5\u6700\u4f4e\u520660.26\u5206\uff0c\u6700\u9ad8\u520693.87\u5206\uff0c\u5e73\u5747\u520676.43\u5206\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u4e00\u822c\u6765\u8bf4\uff0c\u83b7\u5f97\u66f4\u591a\u5956\u5b66\u91d1\u7684\u73a9\u5bb6\u5f80\u5f80\u6709\u66f4\u9ad8\u7684\u8003\u8bd5\u5206\u6570\uff08\u4f8b\u5982\uff0c\u6ca1\u6709\u6536\u5230\u5956\u5b66\u91d1\u7684\u73a9\u5bb6\u7684\u5e73\u5747\u8003\u8bd5\u5206\u6570\u4e3a 70.0\uff0c\u6536\u5230 4 \u4efd\u5956\u5b66\u91d1\u7684\u73a9\u5bb6\u7684\u5e73\u5747\u5ba1\u67e5\u5206\u6570\u4e3a 87.9\uff09\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u8fd8\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u76f4\u65b9\u56fe\uff0c\u6839\u636e\u5212\u5206\u6765\u53ef\u89c6\u5316\u73a9\u5bb6\u6536\u5230\u7684\u62a5\u4ef7\u6570\u91cf\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load <em>ggplot2<\/em> package<\/span>\nlibrary(ggplot2)\n\n<span style=\"color: #008080;\">#create histogram\n<\/span>ggplot(data, aes(offers, fill = division)) +\n  geom_histogram(binwidth=.5, position=\"dodge\")<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u5927\u591a\u6570\u73a9\u5bb6\u6ca1\u6709\u6536\u5230\u6216\u53ea\u6536\u5230\u4e00\u4efd\u62a5\u4ef7\u3002\u8fd9\u662f\u9075\u5faa<a href=\"https:\/\/statorials.org\/cn\/\u9c7c\u7c7b\u5206\u5e03\/\" target=\"_blank\" rel=\"noopener\">\u6cca\u677e\u5206\u5e03<\/a>\u7684\u5178\u578b\u6570\u636e\u96c6\uff1a\u5f88\u5927\u4e00\u90e8\u5206\u54cd\u5e94\u503c\u4e3a\u96f6\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u62df\u5408\u6cca\u677e\u56de\u5f52\u6a21\u578b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>glm()<\/strong>\u51fd\u6570\u8c03\u6574\u6a21\u578b\u5e76\u6307\u5b9a\u6211\u4eec\u8981\u4f7f\u7528<strong>family=&#8221;fish&#8221;<\/strong>\u4f5c\u4e3a\u6a21\u578b\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit the model\n<\/span>model &lt;- glm(offers ~ division + exam, <span style=\"color: #800080;\">family = \"fish\"<\/span> , data = data)\n\n<span style=\"color: #000000;\">#view model output\n<\/span>summary(model)\n\n#Call:\n#glm(formula = offers ~ division + exam, family = \"fish\", data = data)\n#\n#Deviance Residuals: \n# Min 1Q Median 3Q Max  \n#-1.2562 -0.8467 -0.5657 0.3846 2.5033  \n#\n#Coefficients:\n#Estimate Std. Error z value Pr(&gt;|z|)    \n#(Intercept) -7.90602 1.13597 -6.960 3.41e-12 ***\n#divisionB 0.17566 0.27257 0.644 0.519    \n#divisionC -0.05251 0.27819 -0.189 0.850    \n#exam 0.09548 0.01322 7.221 5.15e-13 ***\n#---\n#Significant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n#\n#(Dispersion parameter for fish family taken to be 1)\n#\n# Null deviance: 138,069 on 99 degrees of freedom\n#Residual deviance: 79,247 on 96 degrees of freedom\n#AIC: 204.12\n#\n#Number of Fisher Scoring iterations: 5\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ece\u7ed3\u679c\u4e2d\u6211\u4eec\u53ef\u4ee5\u89c2\u5bdf\u5230\u4ee5\u4e0b\u51e0\u70b9\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u6cca\u677e\u56de\u5f52\u7cfb\u6570\u3001\u4f30\u8ba1\u6807\u51c6\u8bef\u5dee\u3001z \u5206\u6570\u548c\u76f8\u5e94\u7684 p \u503c\u5747\u5df2\u63d0\u4f9b\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\"><em>\u5ba1\u6838<\/em>\u7cfb\u6570\u4e3a<strong>0.09548<\/strong> \uff0c\u8fd9\u8868\u660e<em>\u5ba1\u6838<\/em>\u589e\u52a0 1 \u4e2a\u5355\u4f4d\u7684\u62a5\u4ef7\u6570\u91cf\u7684\u9884\u671f\u5bf9\u6570\u4e3a<strong>0.09548<\/strong> \u3002\u89e3\u91ca\u8fd9\u4e00\u70b9\u7684\u4e00\u79cd\u66f4\u7b80\u5355\u7684\u65b9\u6cd5\u662f\u53d6\u6307\u6570\u503c\uff0c\u5373<strong>e <sup>0.09548<\/sup><\/strong> = <strong>1.10<\/strong> \u3002\u8fd9\u610f\u5473\u7740\u5165\u5b66\u8003\u8bd5\u6bcf\u591a\u52a0\u4e00\u5206\uff0c\u6536\u5230\u7684\u5f55\u53d6\u901a\u77e5\u4e66\u6570\u91cf\u5c31\u4f1a\u589e\u52a0 10%\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><em>B \u7ec4<\/em>\u7684\u7cfb\u6570\u4e3a<strong>0.1756<\/strong> \uff0c\u8fd9\u8868\u660e B \u7ec4\u7403\u5458\u7684\u9884\u671f\u62a5\u4ef7\u6570\u91cf\u6bd4 A \u7ec4\u7403\u5458\u9ad8<strong>0.1756<\/strong> \u3002\u89e3\u91ca\u6b64\u503c\u7684\u66f4\u7b80\u5355\u65b9\u6cd5\u662f\u53d6\u6307\u6570\uff0c\u5373<strong>e <sup>0.1756<\/sup><\/strong> = <strong>1.19<\/strong> \u3002\u8fd9\u610f\u5473\u7740 B \u7ec4\u7684\u73a9\u5bb6\u6536\u5230\u7684\u62a5\u4ef7\u6bd4 A \u7ec4\u7684\u73a9\u5bb6\u591a 19%\u3002\u8bf7\u6ce8\u610f\uff0c\u8fd9\u79cd\u5dee\u5f02\u5728\u7edf\u8ba1\u4e0a\u5e76\u4e0d\u663e\u7740 (p = 0.519)\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><em>C \u7ec4<\/em>\u7684\u7cfb\u6570\u4e3a<strong>-0.05251<\/strong> \uff0c\u8fd9\u8868\u660e C \u7ec4\u7403\u5458\u7684\u62a5\u4ef7\u6570\u91cf\u7684\u9884\u671f\u5bf9\u6570\u6bd4 A \u7ec4\u7403\u5458<i>\u4f4e<\/i><strong>0.05251<\/strong> \u3002\u89e3\u91ca\u6b64\u503c\u7684\u66f4\u7b80\u5355\u65b9\u6cd5\u662f\u53d6\u6307\u6570\u503c\uff0c\u5373<strong>e <sup>0.05251<\/sup><\/strong> = <strong>0.94<\/strong> \u3002\u8fd9\u610f\u5473\u7740 C \u7ec4\u7684\u73a9\u5bb6\u6536\u5230\u7684\u9080\u8bf7\u6bd4 A \u7ec4\u7684\u73a9\u5bb6\u5c11 6%\u3002\u8bf7\u6ce8\u610f\uff0c\u8fd9\u79cd\u5dee\u5f02\u5728\u7edf\u8ba1\u4e0a\u5e76\u4e0d\u663e\u7740 (p = 850)\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u8fd8\u63d0\u4f9b\u4e86\u6709\u5173\u6a21\u578b\u504f\u5dee\u7684\u4fe1\u606f\u3002\u6211\u4eec\u5bf9<em>\u5269\u4f59\u504f\u5dee<\/em>\u7279\u522b\u611f\u5174\u8da3\uff0c\u5b83\u5728<strong>96 \u4e2a<\/strong>\u81ea\u7531\u5ea6\u4e2d\u7684\u503c\u4e3a<strong>79,247<\/strong> \u3002\u4f7f\u7528\u8fd9\u4e9b\u6570\u5b57\uff0c\u6211\u4eec\u53ef\u4ee5\u6267\u884c\u5361\u65b9\u62df\u5408\u4f18\u5ea6\u68c0\u9a8c\uff0c\u4ee5\u67e5\u770b\u6a21\u578b\u662f\u5426\u9002\u5408\u6570\u636e\u3002\u4ee5\u4e0b\u4ee3\u7801\u8bf4\u660e\u4e86\u5982\u4f55\u6267\u884c\u6b64\u6d4b\u8bd5\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong>pchisq(79.24679, 96, lower.tail = FALSE)\n\n#[1] 0.8922676\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b64\u68c0\u9a8c\u7684 p \u503c\u4e3a<strong>0.89<\/strong> \uff0c\u8fdc\u9ad8\u4e8e 0.05 \u663e\u7740\u6027\u6c34\u5e73\u3002\u6211\u4eec\u53ef\u4ee5\u5f97\u51fa\u7ed3\u8bba\uff0c\u6570\u636e\u76f8\u5f53\u9002\u5408\u6a21\u578b\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u67e5\u770b\u7ed3\u679c<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u521b\u5efa\u4e00\u4e2a\u56fe\u8868\uff0c\u663e\u793a\u6839\u636e\u90e8\u95e8\u548c\u5165\u5b66\u8003\u8bd5\u7ed3\u679c\u83b7\u5f97\u7684\u9884\u671f\u5956\u5b66\u91d1\u6570\u91cf\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#find predicted number of offers using the fitted Poisson regression model\n<\/span>data$phat &lt;- predict(model, type=\"response\")\n\n<span style=\"color: #008080;\">#create plot that shows number of offers based on division and exam score\n<\/span>ggplot(data, aes(x = exam, y = phat, color = division)) +\n  geom_point(aes(y = offers), alpha = .7, position = position_jitter(h = .2)) +\n  geom_line() +\n  labs(x = \"Entrance Exam Score\", y = \"Expected number of scholarship offers\")<\/strong><\/pre>\n<h3><\/h3>\n<p><span style=\"color: #000000;\">\u8be5\u56fe\u8868\u663e\u793a\u4e86\u5728\u5165\u5b66\u8003\u8bd5\u4e2d\u83b7\u5f97\u9ad8\u5206\u7684\u7403\u5458\u7684\u9884\u671f\u5956\u5b66\u91d1\u6570\u91cf\u7684\u6700\u9ad8\u6570\u91cf\u3002\u6b64\u5916\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230 B \u533a\uff08\u7eff\u7ebf\uff09\u7684\u7403\u5458\u4e00\u822c\u5e94\u8be5\u6bd4 A \u533a\u6216 C \u533a\u7684\u7403\u5458\u6536\u5230\u66f4\u591a\u7684\u62a5\u4ef7\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u62a5\u544a\u7ed3\u679c<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4ee5\u603b\u7ed3\u6211\u4eec\u7684\u53d1\u73b0\u7684\u65b9\u5f0f\u62a5\u544a\u56de\u5f52\u7ed3\u679c\uff1a<\/span><\/p>\n<blockquote>\n<p><span style=\"color: #000000;\">\u8fd0\u7528\u6cca\u677e\u56de\u5f52\u6765\u6839\u636e\u5206\u533a\u548c\u5165\u5b66\u8003\u8bd5\u6210\u7ee9\u9884\u6d4b\u68d2\u7403\u8fd0\u52a8\u5458\u83b7\u5f97\u7684\u5956\u5b66\u91d1\u6570\u91cf\u3002\u5165\u5b66\u8003\u8bd5\u6bcf\u591a\u83b7\u5f97\u4e00\u5206\uff0c\u6536\u5230\u7684\u5f55\u53d6\u901a\u77e5\u4e66\u6570\u91cf\u5c31\u4f1a\u589e\u52a0 10% ( <em>p &lt; 0.0001)<\/em> \u3002\u672a\u53d1\u73b0\u8fd9\u79cd\u5212\u5206\u5177\u6709\u7edf\u8ba1\u663e\u7740\u6027\u3002<\/span><\/p>\n<\/blockquote>\n<h3><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n<p><a href=\"https:\/\/statorials.org\/cn\/\u7ebf\u6027\u56de\u5f521\/\" target=\"_blank\" rel=\"noopener\">\u7b80\u5355\u7ebf\u6027\u56de\u5f52\u7b80\u4ecb<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u591a\u5143\u7ebf\u6027\u56de\u5f52-1\/\" target=\"_blank\" rel=\"noopener\">\u591a\u5143\u7ebf\u6027\u56de\u5f52\u7b80\u4ecb<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u591a\u9879\u5f0f\u56de\u5f521\/\" target=\"_blank\" rel=\"noopener\">\u591a\u9879\u5f0f\u56de\u5f52\u7b80\u4ecb<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u56de\u5f52\u662f\u4e00\u79cd\u7edf\u8ba1\u65b9\u6cd5\uff0c\u53ef\u7528\u4e8e\u786e\u5b9a\u4e00\u4e2a\u6216\u591a\u4e2a\u9884\u6d4b\u53d8\u91cf\u4e0e\u54cd\u5e94\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002 \u6cca\u677e\u56de\u5f52\u662f\u4e00\u79cd\u7279\u6b8a\u7c7b\u578b\u7684\u56de\u5f52\uff0c\u5176\u4e2d\u54cd\u5e94 [&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-496","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\/ 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