{"id":3816,"date":"2023-07-15T09:08:37","date_gmt":"2023-07-15T09:08:37","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab%e1%84%8b%e1%85%b4-%e1%84%8e%e1%85%ac%e1%84%89%e1%85%a9-%e1%84%80%e1%85%a1%e1%84%8c%e1%85%ae%e1%86%bc%e1%84%8e%e1%85%b5-%e1%84%8c\/"},"modified":"2023-07-15T09:08:37","modified_gmt":"2023-07-15T09:08:37","slug":"%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab%e1%84%8b%e1%85%b4-%e1%84%8e%e1%85%ac%e1%84%89%e1%85%a9-%e1%84%80%e1%85%a1%e1%84%8c%e1%85%ae%e1%86%bc%e1%84%8e%e1%85%b5-%e1%84%8c","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab%e1%84%8b%e1%85%b4-%e1%84%8e%e1%85%ac%e1%84%89%e1%85%a9-%e1%84%80%e1%85%a1%e1%84%8c%e1%85%ae%e1%86%bc%e1%84%8e%e1%85%b5-%e1%84%8c\/","title":{"rendered":"Python\uc5d0\uc11c \uac00\uc911\uce58 \ucd5c\uc18c \uc81c\uacf1 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

\uc120\ud615 \ud68c\uadc0 \ubd84\uc11d\uc758 \uc8fc\uc694 \uac00\uc815<\/a> \uc911 \ud558\ub098\ub294 \uc794\ucc28\uac00<\/a> \uc608\uce21 \ubcc0\uc218\uc758 \uac01 \uc218\uc900\uc5d0\uc11c \ub4f1\ubd84\uc0b0\uc73c\ub85c \ubd84\ud3ec\ub41c\ub2e4\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774 \uac00\uc815\uc744 \ub4f1\ubd84\uc0b0\uc131(homoscedasticity)<\/strong> \uc774\ub77c\uace0 \ud569\ub2c8\ub2e4.<\/span><\/p>\n

\uc774 \uac00\uc815\uc774 \uc874\uc911\ub418\uc9c0 \uc54a\uc73c\uba74 \uc794\ucc28\uc5d0 \uc774\ubd84\uc0b0\uc131\uc774<\/a> \uc874\uc7ac\ud55c\ub2e4\uace0 \ud569\ub2c8\ub2e4. \uc774\ub7f0 \uc77c\uc774 \ubc1c\uc0dd\ud558\uba74 \ud68c\uadc0 \uacb0\uacfc\ub97c \uc2e0\ub8b0\ud560 \uc218 \uc5c6\uac8c \ub429\ub2c8\ub2e4.<\/span><\/p>\n

\uc774 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ud55c \uac00\uc9c0 \ubc29\ubc95\uc740 \uac00\uc911\uce58 \ucd5c\uc18c \uc81c\uacf1 \ud68c\uadc0\ub97c<\/strong> \uc0ac\uc6a9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774\ub294 \uc624\ub958 \ubd84\uc0b0\uc774 \ub0ae\uc740 \uad00\uce21\uce58<\/a> \uac00 \uc624\ub958 \ubd84\uc0b0\uc774 \ud070 \uad00\uce21\uce58\uc5d0 \ube44\ud574 \ub354 \ub9ce\uc740 \uc815\ubcf4\ub97c \ud3ec\ud568\ud558\uae30 \ub54c\ubb38\uc5d0 \ub354 \ub9ce\uc740 \uac00\uc911\uce58\ub97c \ubc1b\ub3c4\ub85d \uad00\uce21\uce58\uc5d0 \uac00\uc911\uce58\ub97c \ud560\ub2f9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.<\/span><\/p>\n

\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c \uac00\uc911\uce58 \ucd5c\uc18c \uc81c\uacf1 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \ub2e8\uacc4\ubcc4 \uc608\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/span><\/p>\n

1\ub2e8\uacc4: \ub370\uc774\ud130 \uc0dd\uc131<\/strong><\/span><\/h2>\n

\uba3c\uc800, \uc218\uc5c5 \uc2dc\uac04\uc5d0 16\uba85\uc758 \ud559\uc0dd\uc774 \uacf5\ubd80\ud55c \uc2dc\uac04\uacfc \ucd5c\uc885 \uc2dc\ud5d8 \uc131\uc801\uc5d0 \ub300\ud55c \uc815\ubcf4\uac00 \ud3ec\ud568\ub41c \ub2e4\uc74c pandas DataFrame\uc744 \ub9cc\ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/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
 2\ub2e8\uacc4: \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378 \ud53c\ud305<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c\uc73c\ub85c, statsmodels<\/strong> \ubaa8\ub4c8\uc758 \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2dc\uac04\uc744<\/strong> \uc608\uce21 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\uace0 \uc810\uc218\ub97c<\/strong> \uc751\ub2f5 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud53c\ud305\ud569\ub2c8\ub2e4.<\/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
 \ubaa8\ub378 \uc694\uc57d\uc5d0\uc11c \ubaa8\ub378\uc758 R \uc81c\uacf1 \uac12\uc774 0.630<\/strong> \uc784\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 3\ub2e8\uacc4: \uac00\uc911\uce58 \ucd5c\uc18c\uc81c\uacf1 \ubaa8\ub378 \ud53c\ud305<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c\uc73c\ub85c, statsmodels<\/strong> WLS()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubd84\uc0b0\uc774 \ub0ae\uc740 \uad00\uce21\uac12\uc774 \ub354 \ub9ce\uc740 \uac00\uc911\uce58\ub97c \ubc1b\ub3c4\ub85d \uac00\uc911\uce58\ub97c \uc124\uc815\ud558\uc5ec \uac00\uc911\uce58 \ucd5c\uc18c \uc81c\uacf1\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/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
 \uacb0\uacfc\uc5d0\uc11c \uc774 \uac00\uc911\uce58 \ucd5c\uc18c \uc81c\uacf1 \ubaa8\ub378\uc758 R \uc81c\uacf1 \uac12\uc774 0.676<\/strong> \uc73c\ub85c \uc99d\uac00\ud55c \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774\ub294 \uac00\uc911 \ucd5c\uc18c \uc81c\uacf1 \ubaa8\ub378\uc774 \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\ubcf4\ub2e4 \uc2dc\ud5d8 \uc810\uc218\uc758 \ubcc0\ub3d9\uc744 \ub354 \ub9ce\uc774 \uc124\uba85\ud560 \uc218 \uc788\uc74c\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774\ub294 \uac00\uc911 \ucd5c\uc18c \uc81c\uacf1 \ubaa8\ub378\uc774 \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc5d0 \ube44\ud574 \ub370\uc774\ud130\uc5d0 \ub354 \uc798 \ub9de\ub294\ub2e4\ub294 \uac83\uc744 \uc54c\ub824\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c \ub2e4\ub978 \uc77c\ubc18\uc801\uc778 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 Python\uc5d0\uc11c \uc794\ucc28 \ud50c\ub86f\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95<\/a>
 Python\uc5d0\uc11c QQ \ud50c\ub86f\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95<\/a>
 Python\uc5d0\uc11c \ub2e4\uc911 \uacf5\uc120\uc131\uc744 \ud14c\uc2a4\ud2b8\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\uc120\ud615 \ud68c\uadc0 \ubd84\uc11d\uc758 \uc8fc\uc694 \uac00\uc815 \uc911 \ud558\ub098\ub294 \uc794\ucc28\uac00 \uc608\uce21 \ubcc0\uc218\uc758 \uac01 \uc218\uc900\uc5d0\uc11c \ub4f1\ubd84\uc0b0\uc73c\ub85c \ubd84\ud3ec\ub41c\ub2e4\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774 \uac00\uc815\uc744 \ub4f1\ubd84\uc0b0\uc131(homoscedasticity) \uc774\ub77c\uace0 \ud569\ub2c8\ub2e4. \uc774 \uac00\uc815\uc774 \uc874\uc911\ub418\uc9c0 \uc54a\uc73c\uba74 \uc794\ucc28\uc5d0 \uc774\ubd84\uc0b0\uc131\uc774 \uc874\uc7ac\ud55c\ub2e4\uace0 \ud569\ub2c8\ub2e4. \uc774\ub7f0 \uc77c\uc774 \ubc1c\uc0dd\ud558\uba74 \ud68c\uadc0 \uacb0\uacfc\ub97c \uc2e0\ub8b0\ud560 \uc218 \uc5c6\uac8c \ub429\ub2c8\ub2e4. \uc774 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ud55c \uac00\uc9c0 \ubc29\ubc95\uc740 \uac00\uc911\uce58 \ucd5c\uc18c \uc81c\uacf1 \ud68c\uadc0\ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774\ub294 \uc624\ub958 \ubd84\uc0b0\uc774 \ub0ae\uc740 \uad00\uce21\uce58 \uac00 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-3816","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPython\uc5d0\uc11c \uac00\uc911 \ucd5c\uc18c \uc81c\uacf1 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95 - 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