{"id":3818,"date":"2023-07-15T09:08:37","date_gmt":"2023-07-15T09:08:37","guid":{"rendered":"https:\/\/statorials.org\/my\/%e1%80%85%e1%80%95%e1%80%ab%e1%80%b8%e1%80%a1%e1%80%af%e1%80%b6%e1%80%b8%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-%e1%80%a1%e1%80%9c%e1%80%b1%e1%80%b8%e1%80%81%e1%80%bb%e1%80%ad%e1%80%94%e1%80%ba%e1%80%a1\/"},"modified":"2023-07-15T09:08:37","modified_gmt":"2023-07-15T09:08:37","slug":"%e1%80%85%e1%80%95%e1%80%ab%e1%80%b8%e1%80%a1%e1%80%af%e1%80%b6%e1%80%b8%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-%e1%80%a1%e1%80%9c%e1%80%b1%e1%80%b8%e1%80%81%e1%80%bb%e1%80%ad%e1%80%94%e1%80%ba%e1%80%a1","status":"publish","type":"post","link":"https:\/\/statorials.org\/my\/%e1%80%85%e1%80%95%e1%80%ab%e1%80%b8%e1%80%a1%e1%80%af%e1%80%b6%e1%80%b8%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-%e1%80%a1%e1%80%9c%e1%80%b1%e1%80%b8%e1%80%81%e1%80%bb%e1%80%ad%e1%80%94%e1%80%ba%e1%80%a1\/","title":{"rendered":"Python \u1010\u103d\u1004\u103a \u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a\u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1019\u103b\u102c\u1038 \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1021\u102c\u1038 \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1019\u100a\u103a\u1014\u100a\u103a\u1038\u104b"},"content":{"rendered":"

<\/p>\n

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linear regression \u104f \u1021\u1013\u102d\u1000 \u101a\u1030\u1006\u1001\u103b\u1000\u103a<\/a> \u1010\u1005\u103a\u1001\u102f\u1019\u103e\u102c \u1021\u1000\u103c\u103d\u1004\u103a\u1038\u1021\u1000\u103b\u1014\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f<\/a> \u1000\u103c\u102d\u102f\u1010\u1004\u103a\u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a \u1021\u1006\u1004\u1037\u103a\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1010\u103d\u1004\u103a \u1010\u1030\u100a\u102e\u101e\u1031\u102c\u1000\u103d\u1032\u101c\u103d\u1032\u1019\u103e\u102f\u1016\u103c\u1004\u1037\u103a \u1016\u103c\u1014\u1037\u103a\u101d\u1031\u1015\u1031\u1038\u1001\u103c\u1004\u103a\u1038\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b \u1024\u101a\u1030\u1006\u1001\u103b\u1000\u103a\u1000\u102d\u102f homoscedasticity<\/strong> \u101f\u102f\u1001\u1031\u102b\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n

\u1024\u101a\u1030\u1006\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1019\u101c\u1031\u1038\u1005\u102c\u1038\u1015\u102b\u1000 \u1021\u1000\u103c\u103d\u1004\u103a\u1038\u1021<\/a> \u1000\u103b\u1014\u103a\u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a \u101b\u103e\u102d\u1014\u1031\u101e\u100a\u103a\u101f\u102f \u1006\u102d\u102f\u1015\u102b\u101e\u100a\u103a\u104b \u1011\u102d\u102f\u101e\u102d\u102f\u1037\u1016\u103c\u1005\u103a\u101c\u102c\u101e\u1031\u102c\u1021\u1001\u102b\u104a \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u101b\u101c\u1012\u103a\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u101a\u102f\u1036\u1000\u103c\u100a\u103a\u1005\u102d\u1010\u103a\u1001\u103b\u101b\u1001\u103c\u1004\u103a\u1038\u1019\u101b\u103e\u102d\u1015\u1031\u104b<\/span><\/p>\n

\u1024\u1015\u103c\u103f\u1014\u102c\u1000\u102d\u102f\u1016\u103c\u1031\u101b\u103e\u1004\u103a\u1038\u101b\u1014\u103a\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u1019\u103e\u102c \u1021\u1019\u103e\u102c\u1038\u1000\u103d\u1032\u101c\u103d\u1032\u1019\u103e\u102f\u1014\u100a\u103a\u1038\u101e\u1031\u102c \u101c\u1031\u1037\u101c\u102c\u101e\u102f\u1036\u1038\u101e\u1015\u103a\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038<\/a> \u1021\u1010\u103d\u1000\u103a \u1021\u101c\u1031\u1038\u1019\u103b\u102c\u1038\u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1015\u1031\u1038\u101e\u100a\u1037\u103a \u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a\u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f<\/strong> \u1000\u102d\u102f\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1001\u103c\u1004\u103a\u1038\u1016\u103c\u1005\u103a\u1015\u103c\u102e\u1038 \u1021\u1019\u103e\u102c\u1038\u1000\u103d\u1032\u101c\u103d\u1032\u1019\u103e\u102f\u1014\u100a\u103a\u1038\u1015\u102b\u1038\u101e\u1030\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a\u1015\u102d\u102f\u1019\u102d\u102f\u101b\u101b\u103e\u102d\u101e\u1031\u102c\u1000\u103c\u1031\u102c\u1004\u1037\u103a \u104e\u1004\u103a\u1038\u1010\u102d\u102f\u1037\u1010\u103d\u1004\u103a\u1021\u1001\u103b\u1000\u103a\u1021\u101c\u1000\u103a\u1019\u103b\u102c\u1038\u1015\u102d\u102f\u1019\u102d\u102f\u1015\u102b\u101d\u1004\u103a\u101e\u1031\u102c\u1000\u103c\u1031\u102c\u1004\u1037\u103a \u104e\u1004\u103a\u1038\u1010\u102d\u102f\u1037\u1010\u103d\u1004\u103a \u1021\u1019\u103e\u102c\u1038\u1021\u101a\u103d\u1004\u103a\u1038\u1000\u103d\u1032\u101c\u103d\u1032\u1019\u103e\u102f\u1015\u102d\u102f\u1019\u103b\u102c\u1038\u101e\u1031\u102c\u101c\u1031\u1037\u101c\u102c\u101e\u102f\u1036\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a\u1014\u103e\u102d\u102f\u1004\u103a\u1038\u101a\u103e\u1009\u103a\u1015\u102b\u104b<\/span><\/p>\n

\u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u101e\u100a\u103a Python \u101b\u103e\u102d \u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a\u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1015\u102f\u1036\u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a\u1000\u102d\u102f \u1025\u1015\u1019\u102c\u1015\u1031\u1038\u1011\u102c\u1038\u101e\u100a\u103a\u104b<\/span><\/p>\n

\u1021\u1006\u1004\u1037\u103a 1: \u1012\u1031\u1010\u102c\u1000\u102d\u102f\u1016\u1014\u103a\u1010\u102e\u1038\u1015\u102b\u104b<\/strong><\/span><\/h2>\n

\u1026\u1038\u1005\u103d\u102c\u104a \u1021\u1010\u1014\u103a\u1038\u1010\u1005\u103a\u1001\u1014\u103a\u1038\u101b\u103e\u102d \u1000\u103b\u1031\u102c\u1004\u103a\u1038\u101e\u102c\u1038 \u1041\u1046 \u1026\u1038\u1021\u1010\u103d\u1000\u103a \u101c\u1031\u1037\u101c\u102c\u1001\u103b\u102d\u1014\u103a\u1014\u102c\u101b\u102e\u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u1014\u103e\u1004\u1037\u103a \u1014\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1005\u102c\u1019\u1031\u1038\u1015\u103d\u1032\u1021\u1006\u1004\u1037\u103a\u1006\u102d\u102f\u1004\u103a\u101b\u102c \u1021\u1001\u103b\u1000\u103a\u1021\u101c\u1000\u103a\u1019\u103b\u102c\u1038\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1015\u1014\u103a\u1012\u102b DataFrame \u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1000\u103c\u1015\u102b\u1005\u102d\u102f\u1037\u104b<\/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
 \u1021\u1006\u1004\u1037\u103a 2- \u101b\u102d\u102f\u1038\u101b\u103e\u1004\u103a\u1038\u101e\u1031\u102c linear regression model \u1000\u102d\u102f \u1000\u102d\u102f\u1000\u103a\u100a\u102e\u1015\u102b\u104b<\/strong><\/span><\/h2>\n
 \u1011\u102d\u102f\u1037\u1014\u1031\u102c\u1000\u103a\u104a \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u101e\u1030\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1014\u103e\u1004\u1037\u103a \u1010\u102f\u1036\u1037\u1015\u103c\u1014\u103a\u1019\u103e\u102f\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1021\u1016\u103c\u1005\u103a \u101b\u1019\u103e\u1010\u103a<\/strong> \u1021\u1016\u103c\u1005\u103a \u1014\u102c\u101b\u102e\u1019\u103b\u102c\u1038\u1000\u102d\u102f<\/strong> \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1000\u102c \u101b\u102d\u102f\u1038\u101b\u103e\u1004\u103a\u1038\u101e\u1031\u102c\u1019\u103b\u1009\u103a\u1038\u1000\u103c\u1031\u102c\u1004\u103a\u1038\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1015\u102f\u1036\u1005\u1036\u1014\u103e\u1004\u1037\u103a\u1000\u102d\u102f\u1000\u103a\u100a\u102e\u101b\u1014\u103a statsmodels<\/strong> \u1019\u1031\u102c\u103a\u1002\u103b\u1030\u1038\u101b\u103e\u102d \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u1019\u100a\u103a\u104b<\/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
 \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1021\u1000\u103b\u1009\u103a\u1038\u1001\u103b\u102f\u1015\u103a\u1019\u103e\u104a \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u104f R-squared \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u101e\u100a\u103a 0.630<\/strong> \u1016\u103c\u1005\u103a\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037 \u1010\u103d\u1031\u1037\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1021\u1006\u1004\u1037\u103a 3- \u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a\u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038 \u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1015\u102f\u1036\u1005\u1036\u1000\u102d\u102f \u1000\u102d\u102f\u1000\u103a\u100a\u102e\u1015\u102b\u104b<\/strong><\/span><\/h2>\n
 \u1011\u102d\u102f\u1037\u1014\u1031\u102c\u1000\u103a\u104a \u1021\u1031\u102c\u1000\u103a\u1015\u102d\u102f\u1004\u103a\u1038\u1000\u103d\u1032\u101c\u103d\u1032\u1019\u103e\u102f\u1016\u103c\u1004\u1037\u103a \u1019\u103e\u1010\u103a\u101e\u102c\u1038\u1019\u103e\u102f\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a\u1015\u102d\u102f\u1019\u102d\u102f\u101b\u101b\u103e\u102d\u1005\u1031\u101e\u100a\u1037\u103a \u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1001\u103c\u1004\u103a\u1038\u1016\u103c\u1004\u1037\u103a \u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a \u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1014\u103a statsmodels<\/strong> WLS()<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/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
 \u101b\u101c\u1012\u103a\u1019\u103e\u104a \u1024\u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a\u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1015\u102f\u1036\u1005\u1036\u1021\u1010\u103d\u1000\u103a R-squared \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u101e\u100a\u103a 0.676<\/strong> \u101e\u102d\u102f\u1037 \u1010\u102d\u102f\u1038\u101c\u102c\u101e\u100a\u103a\u1000\u102d\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1010\u103d\u1031\u1037\u1019\u103c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u104e\u1004\u103a\u1038\u101e\u100a\u103a \u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a\u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1015\u102f\u1036\u1005\u1036\u101e\u100a\u103a \u101b\u102d\u102f\u1038\u101b\u103e\u1004\u103a\u1038\u101e\u1031\u102c\u1019\u103b\u1009\u103a\u1038\u1016\u103c\u1031\u102c\u1004\u1037\u103a\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1015\u102f\u1036\u1005\u1036\u1011\u1000\u103a \u1005\u102c\u1019\u1031\u1038\u1015\u103d\u1032\u101b\u1019\u103e\u1010\u103a\u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a \u1000\u103d\u1032\u101c\u103d\u1032\u1019\u103e\u102f\u1000\u102d\u102f \u1015\u102d\u102f\u1019\u102d\u102f\u101b\u103e\u1004\u103a\u1038\u1015\u103c\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a\u1000\u102d\u102f \u1016\u1031\u102c\u103a\u1015\u103c\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u104e\u1004\u103a\u1038\u101e\u100a\u103a \u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a\u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1015\u102f\u1036\u1005\u1036\u101e\u100a\u103a \u101b\u102d\u102f\u1038\u101b\u103e\u1004\u103a\u1038\u101e\u1031\u102c\u1019\u103b\u1009\u103a\u1038\u1000\u103c\u1031\u102c\u1004\u103a\u1038\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1015\u102f\u1036\u1005\u1036\u1014\u103e\u1004\u1037\u103a \u1014\u103e\u102d\u102f\u1004\u103a\u1038\u101a\u103e\u1009\u103a\u1015\u102b\u1000 \u1012\u1031\u1010\u102c\u1014\u103e\u1004\u1037\u103a\u1015\u102d\u102f\u104d\u1000\u102d\u102f\u1000\u103a\u100a\u102e\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1000\u102d\u102f\u1015\u103c\u1031\u102c\u1015\u103c\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1011\u1015\u103a\u101c\u1031\u102c\u1004\u103a\u1038\u1021\u101b\u1004\u103a\u1038\u1021\u1019\u103c\u1005\u103a\u1019\u103b\u102c\u1038<\/strong><\/span><\/h2>\n
 \u1021\u1031\u102c\u1000\u103a\u1015\u102b \u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1019\u103b\u102c\u1038\u101e\u100a\u103a Python \u1010\u103d\u1004\u103a \u1021\u1001\u103c\u102c\u1038\u101e\u1031\u102c \u1021\u101e\u102f\u1036\u1038\u1019\u103b\u102c\u1038\u101e\u1031\u102c \u1021\u101c\u102f\u1015\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u101e\u100a\u103a-<\/span><\/p>\n
 Python \u1010\u103d\u1004\u103a Residual Plot \u1016\u1014\u103a\u1010\u102e\u1038\u1014\u100a\u103a\u1038<\/a>
 Python \u1010\u103d\u1004\u103a QQ Plot \u1016\u1014\u103a\u1010\u102e\u1038\u1014\u100a\u103a\u1038<\/a>
 Python \u1010\u103d\u1004\u103a multicollinearity \u1000\u102d\u102f\u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1014\u100a\u103a\u1038<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
linear regression \u104f \u1021\u1013\u102d\u1000 \u101a\u1030\u1006\u1001\u103b\u1000\u103a \u1010\u1005\u103a\u1001\u102f\u1019\u103e\u102c \u1021\u1000\u103c\u103d\u1004\u103a\u1038\u1021\u1000\u103b\u1014\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1000\u103c\u102d\u102f\u1010\u1004\u103a\u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a \u1021\u1006\u1004\u1037\u103a\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1010\u103d\u1004\u103a \u1010\u1030\u100a\u102e\u101e\u1031\u102c\u1000\u103d\u1032\u101c\u103d\u1032\u1019\u103e\u102f\u1016\u103c\u1004\u1037\u103a \u1016\u103c\u1014\u1037\u103a\u101d\u1031\u1015\u1031\u1038\u1001\u103c\u1004\u103a\u1038\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b \u1024\u101a\u1030\u1006\u1001\u103b\u1000\u103a\u1000\u102d\u102f homoscedasticity \u101f\u102f\u1001\u1031\u102b\u103a\u101e\u100a\u103a\u104b \u1024\u101a\u1030\u1006\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1019\u101c\u1031\u1038\u1005\u102c\u1038\u1015\u102b\u1000 \u1021\u1000\u103c\u103d\u1004\u103a\u1038\u1021 \u1000\u103b\u1014\u103a\u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a \u101b\u103e\u102d\u1014\u1031\u101e\u100a\u103a\u101f\u102f \u1006\u102d\u102f\u1015\u102b\u101e\u100a\u103a\u104b \u1011\u102d\u102f\u101e\u102d\u102f\u1037\u1016\u103c\u1005\u103a\u101c\u102c\u101e\u1031\u102c\u1021\u1001\u102b\u104a \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u101b\u101c\u1012\u103a\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u101a\u102f\u1036\u1000\u103c\u100a\u103a\u1005\u102d\u1010\u103a\u1001\u103b\u101b\u1001\u103c\u1004\u103a\u1038\u1019\u101b\u103e\u102d\u1015\u1031\u104b \u1024\u1015\u103c\u103f\u1014\u102c\u1000\u102d\u102f\u1016\u103c\u1031\u101b\u103e\u1004\u103a\u1038\u101b\u1014\u103a\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u1019\u103e\u102c \u1021\u1019\u103e\u102c\u1038\u1000\u103d\u1032\u101c\u103d\u1032\u1019\u103e\u102f\u1014\u100a\u103a\u1038\u101e\u1031\u102c \u101c\u1031\u1037\u101c\u102c\u101e\u102f\u1036\u1038\u101e\u1015\u103a\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038 \u1021\u1010\u103d\u1000\u103a \u1021\u101c\u1031\u1038\u1019\u103b\u102c\u1038\u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1015\u1031\u1038\u101e\u100a\u1037\u103a \u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a\u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f \u1000\u102d\u102f\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1001\u103c\u1004\u103a\u1038\u1016\u103c\u1005\u103a\u1015\u103c\u102e\u1038 \u1021\u1019\u103e\u102c\u1038\u1000\u103d\u1032\u101c\u103d\u1032\u1019\u103e\u102f\u1014\u100a\u103a\u1038\u1015\u102b\u1038\u101e\u1030\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a\u1015\u102d\u102f\u1019\u102d\u102f\u101b\u101b\u103e\u102d\u101e\u1031\u102c\u1000\u103c\u1031\u102c\u1004\u1037\u103a \u104e\u1004\u103a\u1038\u1010\u102d\u102f\u1037\u1010\u103d\u1004\u103a\u1021\u1001\u103b\u1000\u103a\u1021\u101c\u1000\u103a\u1019\u103b\u102c\u1038\u1015\u102d\u102f\u1019\u102d\u102f\u1015\u102b\u101d\u1004\u103a\u101e\u1031\u102c\u1000\u103c\u1031\u102c\u1004\u1037\u103a \u104e\u1004\u103a\u1038\u1010\u102d\u102f\u1037\u1010\u103d\u1004\u103a \u1021\u1019\u103e\u102c\u1038\u1021\u101a\u103d\u1004\u103a\u1038\u1000\u103d\u1032\u101c\u103d\u1032\u1019\u103e\u102f\u1015\u102d\u102f\u1019\u103b\u102c\u1038\u101e\u1031\u102c\u101c\u1031\u1037\u101c\u102c\u101e\u102f\u1036\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a\u1014\u103e\u102d\u102f\u1004\u103a\u1038\u101a\u103e\u1009\u103a\u1015\u102b\u104b \u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u101e\u100a\u103a Python \u101b\u103e\u102d \u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a\u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1015\u102f\u1036\u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a\u1000\u102d\u102f \u1025\u1015\u1019\u102c\u1015\u1031\u1038\u1011\u102c\u1038\u101e\u100a\u103a\u104b \u1021\u1006\u1004\u1037\u103a 1: \u1012\u1031\u1010\u102c\u1000\u102d\u102f\u1016\u1014\u103a\u1010\u102e\u1038\u1015\u102b\u104b \u1026\u1038\u1005\u103d\u102c\u104a \u1021\u1010\u1014\u103a\u1038\u1010\u1005\u103a\u1001\u1014\u103a\u1038\u101b\u103e\u102d \u1000\u103b\u1031\u102c\u1004\u103a\u1038\u101e\u102c\u1038 \u1041\u1046 \u1026\u1038\u1021\u1010\u103d\u1000\u103a \u101c\u1031\u1037\u101c\u102c\u1001\u103b\u102d\u1014\u103a\u1014\u102c\u101b\u102e\u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u1014\u103e\u1004\u1037\u103a \u1014\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1005\u102c\u1019\u1031\u1038\u1015\u103d\u1032\u1021\u1006\u1004\u1037\u103a\u1006\u102d\u102f\u1004\u103a\u101b\u102c \u1021\u1001\u103b\u1000\u103a\u1021\u101c\u1000\u103a\u1019\u103b\u102c\u1038\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1015\u1014\u103a\u1012\u102b DataFrame \u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1000\u103c\u1015\u102b\u1005\u102d\u102f\u1037\u104b […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"yoast_head":"\nPython - Statology \u1010\u103d\u1004\u103a \u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a\u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1019\u103b\u102c\u1038 \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u1019\u100a\u103a\u1014\u100a\u103a\u1038\u104b<\/title>\n<meta name=\"description\" content=\"\u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1010\u103d\u1004\u103a \u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a\u1025\u1015\u1019\u102c\u1010\u1005\u103a\u1001\u102f\u1021\u1015\u102b\u1021\u101d\u1004\u103a Python \u101b\u103e\u102d \u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a\u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1021\u102c\u1038 \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u1011\u102c\u1038\u101e\u100a\u103a\u104b\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/my\/\u1005\u1015\u102b\u1038\u1021\u102f\u1036\u1038\u1010\u103d\u1004\u103a-\u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a\u1021\/\" \/>\n<meta property=\"og:locale\" content=\"my_MM\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python - Statology \u1010\u103d\u1004\u103a \u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a\u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1019\u103b\u102c\u1038 \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1000\u102d\u102f 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content=\"https:\/\/statorials.org\/my\/\u1005\u1015\u102b\u1038\u1021\u102f\u1036\u1038\u1010\u103d\u1004\u103a-\u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a\u1021\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-15T09:08:37+00:00\" \/>\n<meta name=\"author\" content=\"Benjamin Anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Benjamin Anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" 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