{"id":1336,"date":"2023-07-26T19:53:42","date_gmt":"2023-07-26T19:53:42","guid":{"rendered":"https:\/\/statorials.org\/ja\/python%e3%81%ae%e6%ae%8b%e5%b7%ae%e4%ba%8c%e4%b9%97%e5%92%8c\/"},"modified":"2023-07-26T19:53:42","modified_gmt":"2023-07-26T19:53:42","slug":"python%e3%81%ae%e6%ae%8b%e5%b7%ae%e4%ba%8c%e4%b9%97%e5%92%8c","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/python%e3%81%ae%e6%ae%8b%e5%b7%ae%e4%ba%8c%e4%b9%97%e5%92%8c\/","title":{"rendered":"Python \u3067\u6b8b\u5dee\u4e8c\u4e57\u548c\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ja\/\u6b8b\u57fa\/\" target=\"_blank\" rel=\"noopener\">\u6b8b\u5dee<\/a>\u306f\u3001\u56de\u5e30\u30e2\u30c7\u30eb\u306b\u304a\u3051\u308b\u89b3\u6e2c\u5024\u3068\u4e88\u6e2c\u5024\u306e\u5dee\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u3088\u3046\u306b\u8a08\u7b97\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b8b\u5dee = \u89b3\u6e2c\u5024 \u2013 \u4e88\u6e2c\u5024<\/span><\/p>\n<p><span style=\"color: #000000;\">\u56de\u5e30\u30e2\u30c7\u30eb\u304c\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u3069\u306e\u7a0b\u5ea6\u9069\u5408\u3057\u3066\u3044\u308b\u304b\u3092\u7406\u89e3\u3059\u308b 1 \u3064\u306e\u65b9\u6cd5\u306f\u3001<strong>\u6b8b\u5dee\u4e8c\u4e57\u548c<\/strong>\u3092\u8a08\u7b97\u3059\u308b\u3053\u3068\u3067\u3059\u3002\u3053\u308c\u306f\u6b21\u306e\u3088\u3046\u306b\u8a08\u7b97\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b8b\u5dee\u4e8c\u4e57\u548c = \u03a3(e <sub>i<\/sub> ) <sup>2<\/sup><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\u03a3<\/strong> : \u300c\u548c\u300d\u3092\u610f\u5473\u3059\u308b\u30ae\u30ea\u30b7\u30e3\u8a9e\u306e\u8a18\u53f7<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>e <sub>i<\/sub><\/strong> : i \u756a\u76ee<sup>\u306e<\/sup>\u5270\u4f59<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u5024\u304c\u4f4e\u3044\u307b\u3069\u3001\u30e2\u30c7\u30eb\u306f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u9069\u5408\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u6b8b\u5dee\u4e8c\u4e57\u548c\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u3092\u6bb5\u968e\u7684\u306b\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 1: \u30c7\u30fc\u30bf\u3092\u5165\u529b\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u3053\u306e\u4f8b\u3067\u306f\u3001\u5b66\u7fd2\u306b\u8cbb\u3084\u3057\u305f\u6642\u9593\u6570\u3001\u53d7\u9a13\u3057\u305f\u4e88\u5099\u8a66\u9a13\u306e\u5408\u8a08\u6570\u3001\u304a\u3088\u3073 14 \u4eba\u306e\u7570\u306a\u308b\u5b66\u751f\u304c\u53d6\u5f97\u3057\u305f\u8a66\u9a13\u7d50\u679c\u306b\u95a2\u3059\u308b\u30c7\u30fc\u30bf\u3092\u5165\u529b\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> pandas <span style=\"color: #107d3f;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame<\/span>\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #008000;\">hours<\/span> ': [1, 2, 2, 4, 2, 1, 5, 4, 2, 4, 4, 3, 6, 5],\n                   ' <span style=\"color: #008000;\">exams<\/span> ': [1, 3, 3, 5, 2, 2, 1, 1, 0, 3, 4, 3, 2, 4],\n                   ' <span style=\"color: #008000;\">score<\/span> ': [76, 78, 85, 88, 72, 69, 94, 94, 88, 92, 90, 75, 96, 90]})\n<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 2: \u56de\u5e30\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001statsmodels \u30e9\u30a4\u30d6\u30e9\u30ea\u306e<\/span><a href=\"https:\/\/www.statsmodels.org\/devel\/generated\/statsmodels.regression.linear_model.OLS.html\" target=\"_blank\" rel=\"noopener noreferrer\">OLS() \u95a2\u6570<\/a><span style=\"color: #000000;\">\u3092\u4f7f\u7528\u3057\u3066\u3001<\/span> \u300c\u6642\u9593\u300d\u3068\u300c\u8a66\u9a13\u300d\u3092\u4e88\u6e2c\u5909\u6570\u3068\u3057\u3066\u3001\u300c\u30b9\u30b3\u30a2\u300d\u3092\u5fdc\u7b54\u5909\u6570\u3068\u3057\u3066\u4f7f\u7528\u3057\u3066\u3001\u901a\u5e38\u306e\u6700\u5c0f\u4e8c\u4e57\u56de\u5e30\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002<\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> statsmodels. <span style=\"color: #3366ff;\">api<\/span> <span style=\"color: #008000;\">as<\/span> sm\n\n<span style=\"color: #008080;\">#define response variable\n<\/span>y = df[' <span style=\"color: #008000;\">score<\/span> ']\n\n<span style=\"color: #008080;\">#define predictor variables\n<\/span>x = df[[' <span style=\"color: #008000;\">hours<\/span> ', ' <span style=\"color: #008000;\">exams<\/span> ']]\n\n<span style=\"color: #008080;\">#add constant to predictor variables\n<\/span>x = sm. <span style=\"color: #3366ff;\">add_constant<\/span> (x)\n\n<span style=\"color: #008080;\">#fit linear regression model\n<\/span>model = sm. <span style=\"color: #3366ff;\">OLS<\/span> (y,x). <span style=\"color: #3366ff;\">fit<\/span> ()\n\n<span style=\"color: #008080;\">#view model summary\n<\/span><span style=\"color: #993300;\">print<\/span> ( <span style=\"color: #3366ff;\">model.summary<\/span> ())\n\n                            OLS Regression Results                            \n==================================================== ============================\nDept. Variable: R-squared score: 0.722\nModel: OLS Adj. R-squared: 0.671\nMethod: Least Squares F-statistic: 14.27\nDate: Sat, 02 Jan 2021 Prob (F-statistic): 0.000878\nTime: 15:58:35 Log-Likelihood: -41.159\nNo. Comments: 14 AIC: 88.32\nDf Residuals: 11 BIC: 90.24\nModel: 2                                         \nCovariance Type: non-robust                                         \n==================================================== ============================\n                 coef std err t P&gt;|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 71.8144 3.680 19.517 0.000 63.716 79.913\nhours 5.0318 0.942 5.339 0.000 2.958 7.106\nexams -1.3186 1.063 -1.240 0.241 -3.658 1.021\n==================================================== ============================\nOmnibus: 0.976 Durbin-Watson: 1.270\nProb(Omnibus): 0.614 Jarque-Bera (JB): 0.757\nSkew: -0.245 Prob(JB): 0.685\nKurtosis: 1.971 Cond. No. 12.1\n==================================================== ============================\n<\/strong><\/span><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 3: \u6b8b\u5dee\u4e8c\u4e57\u548c\u3092\u8a08\u7b97\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001\u30e2\u30c7\u30eb\u306e\u6b8b\u5dee\u4e8c\u4e57\u548c\u3092\u8a08\u7b97\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #993300;\">print<\/span> ( <span style=\"color: #3366ff;\">model.ssr<\/span> )\n\n293.25612951525414\n<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u6b8b\u308a\u306e\u5e73\u65b9\u548c\u306f<strong>293,256<\/strong>\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n<p><a href=\"https:\/\/statorials.org\/ja\/python\u3066\u3099\u306e\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\/\" target=\"_blank\" rel=\"noopener\">Python \u3067\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u7dda\u5f62\u56de\u5e30python\/\" target=\"_blank\" rel=\"noopener\">Python \u3067\u91cd\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/>\u6b8b\u5dee\u4e8c\u4e57\u548c\u8a08\u7b97\u6a5f<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6b8b\u5dee\u306f\u3001\u56de\u5e30\u30e2\u30c7\u30eb\u306b\u304a\u3051\u308b\u89b3\u6e2c\u5024\u3068\u4e88\u6e2c\u5024\u306e\u5dee\u3067\u3059\u3002 \u6b21\u306e\u3088\u3046\u306b\u8a08\u7b97\u3055\u308c\u307e\u3059\u3002 \u6b8b\u5dee = \u89b3\u6e2c\u5024 \u2013 \u4e88\u6e2c\u5024 \u56de\u5e30\u30e2\u30c7\u30eb\u304c\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u3069\u306e\u7a0b\u5ea6\u9069\u5408\u3057\u3066\u3044\u308b\u304b\u3092\u7406\u89e3\u3059\u308b 1 \u3064\u306e\u65b9\u6cd5\u306f\u3001\u6b8b\u5dee\u4e8c\u4e57\u548c\u3092\u8a08\u7b97\u3059\u308b\u3053\u3068\u3067\u3059\u3002\u3053\u308c\u306f [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-1336","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - 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