{"id":3814,"date":"2023-07-15T09:08:37","date_gmt":"2023-07-15T09:08:37","guid":{"rendered":"https:\/\/statorials.org\/ja\/python%e3%81%ae%e6%9c%80%e5%b0%8f%e9%87%8d%e3%81%bf%e4%ba%8c%e4%b9%97%e6%b3%95\/"},"modified":"2023-07-15T09:08:37","modified_gmt":"2023-07-15T09:08:37","slug":"python%e3%81%ae%e6%9c%80%e5%b0%8f%e9%87%8d%e3%81%bf%e4%ba%8c%e4%b9%97%e6%b3%95","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/python%e3%81%ae%e6%9c%80%e5%b0%8f%e9%87%8d%e3%81%bf%e4%ba%8c%e4%b9%97%e6%b3%95\/","title":{"rendered":"Python \u3067\u52a0\u91cd\u6700\u5c0f\u4e8c\u4e57\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

<\/p>\n

\n

\u7dda\u5f62\u56de\u5e30\u306e\u91cd\u8981\u306a\u524d\u63d0<\/a>\u306e 1 \u3064\u306f\u3001\u4e88\u6e2c\u5909\u6570\u306e\u5404\u30ec\u30d9\u30eb\u3067\u6b8b<\/a>\u5dee\u304c\u7b49\u3057\u3044\u5206\u6563\u3067\u5206\u5e03\u3057\u3066\u3044\u308b\u3068\u3044\u3046\u3053\u3068\u3067\u3059\u3002\u3053\u306e\u4eee\u5b9a\u306f\u7b49\u5206\u6563\u6027<\/strong>\u3068\u3057\u3066\u77e5\u3089\u308c\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n

\u3053\u306e\u4eee\u5b9a\u304c\u5c0a\u91cd\u3055\u308c\u306a\u3044\u5834\u5408\u3001\u6b8b\u5dee\u306b\u4e0d\u5747\u4e00\u5206\u6563\u6027\u304c<\/a>\u5b58\u5728\u3059\u308b\u3068\u8a00\u308f\u308c\u307e\u3059\u3002\u3053\u308c\u304c\u8d77\u3053\u308b\u3068\u3001\u56de\u5e30\u7d50\u679c\u306f\u4fe1\u983c\u3067\u304d\u306a\u304f\u306a\u308a\u307e\u3059\u3002<\/span><\/p>\n

\u3053\u306e\u554f\u984c\u3092\u89e3\u6c7a\u3059\u308b 1 \u3064\u306e\u65b9\u6cd5\u306f\u3001\u91cd\u307f\u4ed8\u304d\u6700\u5c0f\u4e8c\u4e57\u56de\u5e30<\/strong>\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u3067\u3059\u3002\u3053\u308c\u306f\u3001\u8aa4\u5dee\u5206\u6563\u304c\u5c0f\u3055\u3044\u89b3\u6e2c\u5024<\/a>\u306b\u306f\u3001\u3088\u308a\u5927\u304d\u306a\u8aa4\u5dee\u5206\u6563\u3092\u6301\u3064\u89b3\u6e2c\u5024\u3068\u6bd4\u8f03\u3057\u3066\u3088\u308a\u591a\u304f\u306e\u60c5\u5831\u304c\u542b\u307e\u308c\u308b\u305f\u3081\u3001\u3088\u308a\u591a\u304f\u306e\u91cd\u307f\u3092\u53d7\u3051\u53d6\u308b\u3088\u3046\u306b\u89b3\u6e2c\u5024\u306b\u91cd\u307f\u3092\u5272\u308a\u5f53\u3066\u307e\u3059\u3002<\/span><\/p>\n

\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u52a0\u91cd\u6700\u5c0f\u4e8c\u4e57\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u6bb5\u968e\u7684\u306b\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u30b9\u30c6\u30c3\u30d7 1: \u30c7\u30fc\u30bf\u3092\u4f5c\u6210\u3059\u308b<\/strong><\/span><\/h2>\n

\u307e\u305a\u3001\u30af\u30e9\u30b9\u306e 16 \u4eba\u306e\u751f\u5f92\u306e\u5b66\u7fd2\u6642\u9593\u6570\u3068\u6700\u7d42\u8a66\u9a13\u306e\u6210\u7e3e\u306b\u95a2\u3059\u308b\u60c5\u5831\u3092\u542b\u3080\u6b21\u306e\u30d1\u30f3\u30c0 \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u4f5c\u6210\u3057\u307e\u3057\u3087\u3046\u3002<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' hours<\/span> ': [1, 1, 2, 2, 2, 3, 4, 4, 4, 5, 5, 5, 6, 6, 7, 8],\n                   ' score<\/span> ': [48, 78, 72, 70, 66, 92, 93, 75, 75, 80, 95, 97,\n                             90, 96, 99, 99]})\n\n#view first five rows of DataFrame\n<\/span>print<\/span> ( df.head<\/span> ())\n\n   hours score\n0 1 48\n1 1 78\n2 2 72\n3 2 70\n4 2 66<\/strong><\/pre>\n
\u30b9\u30c6\u30c3\u30d7 2: \u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u308b<\/strong><\/span><\/h2>\n
\u6b21\u306b\u3001 statsmodels<\/strong>\u30e2\u30b8\u30e5\u30fc\u30eb\u306e\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u4e88\u6e2c\u5909\u6570\u3068\u3057\u3066\u6642\u9593<\/strong>\u3092\u4f7f\u7528\u3057\u3001\u5fdc\u7b54\u5909\u6570\u3068\u3057\u3066\u30b9\u30b3\u30a2\u3092<\/strong>\u4f7f\u7528\u3059\u308b\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3057\u307e\u3059\u3002<\/span><\/p>\n
 import<\/span> statsmodels.api as<\/span> sm\n\n#define predictor and response variables\n<\/span>y = df[' score<\/span> ']\nX = df[' hours<\/span> ']\n\n#add constant to predictor variables\n<\/span>X = sm. add_constant<\/span> (x)\n\n#fit linear regression model\n<\/span>fit = sm. OLS<\/span> (y,x). fit<\/span> ()\n\n#view model summary\n<\/span>print<\/span> ( fit.summary<\/span> ())\n\n                            OLS Regression Results                            \n==================================================== ============================\nDept. Variable: R-squared score: 0.630\nModel: OLS Adj. R-squared: 0.603\nMethod: Least Squares F-statistic: 23.80\nDate: Mon, 31 Oct 2022 Prob (F-statistic): 0.000244\nTime: 11:19:54 Log-Likelihood: -57.184\nNo. Observations: 16 AIC: 118.4\nDf Residuals: 14 BIC: 119.9\nModel: 1                                         \nCovariance Type: non-robust                                         \n==================================================== ============================\n                 coef std err t P>|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 60.4669 5.128 11.791 0.000 49.468 71.465\nhours 5.5005 1.127 4.879 0.000 3.082 7.919\n==================================================== ============================\nOmnibus: 0.041 Durbin-Watson: 1.910\nProb(Omnibus): 0.980 Jarque-Bera (JB): 0.268\nSkew: -0.010 Prob(JB): 0.875\nKurtosis: 2.366 Cond. No. 10.5<\/strong><\/pre>\n
\u30e2\u30c7\u30eb\u306e\u6982\u8981\u304b\u3089\u3001\u30e2\u30c7\u30eb\u306e R \u4e8c\u4e57\u5024\u304c0.630<\/strong>\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u30b9\u30c6\u30c3\u30d7 3: \u91cd\u307f\u4ed8\u304d\u6700\u5c0f\u4e8c\u4e57\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u308b<\/strong><\/span><\/h2>\n
\u6b21\u306b\u3001 statsmodels<\/strong> WLS()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u5206\u6563\u304c\u5c0f\u3055\u3044\u89b3\u6e2c\u5024\u306e\u91cd\u307f\u304c\u5927\u304d\u304f\u306a\u308b\u3088\u3046\u306b\u91cd\u307f\u3092\u8a2d\u5b9a\u3059\u308b\u3053\u3068\u3067\u3001\u91cd\u307f\u4ed8\u304d\u6700\u5c0f\u4e8c\u4e57\u6cd5\u3092\u5b9f\u884c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #define weights to use\n<\/span>wt = 1\/smf. ols<\/span> (' fit.resid.abs() ~ fit.fittedvalues<\/span> ', data=df). fit<\/span> (). fitted values<\/span> **2\n\n#fit weighted least squares regression model\n<\/span>fit_wls = sm. WLS<\/span> (y, X, weights=wt). fit<\/span> ()\n\n#view summary of weighted least squares regression model\n<\/span>print<\/span> ( fit_wls.summary<\/span> ())\n\n                            WLS Regression Results                            \n==================================================== ============================\nDept. Variable: R-squared score: 0.676\nModel: WLS Adj. R-squared: 0.653\nMethod: Least Squares F-statistic: 29.24\nDate: Mon, 31 Oct 2022 Prob (F-statistic): 9.24e-05\nTime: 11:20:10 Log-Likelihood: -55.074\nNo. Comments: 16 AIC: 114.1\nDf Residuals: 14 BIC: 115.7\nModel: 1                                         \nCovariance Type: non-robust                                         \n==================================================== ============================\n                 coef std err t P>|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 63.9689 5.159 12.400 0.000 52.905 75.033\nhours 4.7091 0.871 5.407 0.000 2.841 6.577\n==================================================== ============================\nOmnibus: 2,482 Durbin-Watson: 1,786\nProb(Omnibus): 0.289 Jarque-Bera (JB): 1.058\nSkew: 0.029 Prob(JB): 0.589\nKurtosis: 1.742 Cond. No. 17.6\n==================================================== ============================<\/strong><\/span><\/pre>\n
\u7d50\u679c\u304b\u3089\u3001\u3053\u306e\u52a0\u91cd\u6700\u5c0f\u4e8c\u4e57\u30e2\u30c7\u30eb\u306e R \u4e8c\u4e57\u5024\u304c0.676<\/strong>\u306b\u5897\u52a0\u3057\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u3053\u308c\u306f\u3001\u52a0\u91cd\u6700\u5c0f\u4e8c\u4e57\u30e2\u30c7\u30eb\u306e\u65b9\u304c\u3001\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3088\u308a\u3082\u8a66\u9a13\u5f97\u70b9\u306e\u5206\u6563\u3092\u3088\u308a\u591a\u304f\u8aac\u660e\u3067\u304d\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
\u3053\u308c\u306f\u3001\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3068\u6bd4\u8f03\u3057\u3066\u3001\u52a0\u91cd\u6700\u5c0f\u4e8c\u4e57\u30e2\u30c7\u30eb\u306e\u65b9\u304c\u30c7\u30fc\u30bf\u3078\u306e\u9069\u5408\u6027\u304c\u9ad8\u3044\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h2>\n
\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u4ed6\u306e\u4e00\u822c\u7684\u306a\u30bf\u30b9\u30af\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n
 Python \u3067\u6b8b\u5dee\u30d7\u30ed\u30c3\u30c8\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5<\/a>
Python \u3067 QQ \u30d7\u30ed\u30c3\u30c8\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5<\/a>
Python \u3067\u591a\u91cd\u5171\u7dda\u6027\u3092\u30c6\u30b9\u30c8\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u7dda\u5f62\u56de\u5e30\u306e\u91cd\u8981\u306a\u524d\u63d0\u306e 1 \u3064\u306f\u3001\u4e88\u6e2c\u5909\u6570\u306e\u5404\u30ec\u30d9\u30eb\u3067\u6b8b\u5dee\u304c\u7b49\u3057\u3044\u5206\u6563\u3067\u5206\u5e03\u3057\u3066\u3044\u308b\u3068\u3044\u3046\u3053\u3068\u3067\u3059\u3002\u3053\u306e\u4eee\u5b9a\u306f\u7b49\u5206\u6563\u6027\u3068\u3057\u3066\u77e5\u3089\u308c\u3066\u3044\u307e\u3059\u3002 \u3053\u306e\u4eee\u5b9a\u304c\u5c0a\u91cd\u3055\u308c\u306a\u3044\u5834\u5408\u3001\u6b8b\u5dee\u306b\u4e0d\u5747\u4e00\u5206\u6563\u6027\u304c\u5b58\u5728\u3059\u308b\u3068\u8a00\u308f\u308c\u307e\u3059\u3002\u3053\u308c\u304c\u8d77 […]<\/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-3814","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\nPython \u3067\u52a0\u91cd\u6700\u5c0f\u4e8c\u4e57\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 - 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