{"id":3817,"date":"2023-07-15T09:08:37","date_gmt":"2023-07-15T09:08:37","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d9%85%d8%b1%d8%a8%d8%b9%d8%a7%d8%aa-%d8%a7%d9%84%d8%a7%d9%94%d9%82%d9%84-%d9%88%d8%b2%d9%86%d8%a7-%d9%81%d9%8a-%d8%a8%d9%8a%d8%ab%d9%88%d9%86\/"},"modified":"2023-07-15T09:08:37","modified_gmt":"2023-07-15T09:08:37","slug":"%d8%a7%d9%84%d9%85%d8%b1%d8%a8%d8%b9%d8%a7%d8%aa-%d8%a7%d9%84%d8%a7%d9%94%d9%82%d9%84-%d9%88%d8%b2%d9%86%d8%a7-%d9%81%d9%8a-%d8%a8%d9%8a%d8%ab%d9%88%d9%86","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d9%85%d8%b1%d8%a8%d8%b9%d8%a7%d8%aa-%d8%a7%d9%84%d8%a7%d9%94%d9%82%d9%84-%d9%88%d8%b2%d9%86%d8%a7-%d9%81%d9%8a-%d8%a8%d9%8a%d8%ab%d9%88%d9%86\/","title":{"rendered":"\u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0645\u0631\u0628\u0639\u0627\u062a \u0627\u0644\u0635\u063a\u0631\u0649 \u0627\u0644\u0645\u0631\u062c\u062d\u0629 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646"},"content":{"rendered":"

<\/p>\n

\n

\u0623\u062d\u062f \u0627\u0644\u0627\u0641\u062a\u0631\u0627\u0636\u0627\u062a \u0627\u0644\u0631\u0626\u064a\u0633\u064a\u0629 \u0644\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a<\/a> \u0647\u0648 \u0623\u0646 \u0627\u0644\u0628\u0642\u0627\u064a\u0627<\/a> \u064a\u062a\u0645 \u062a\u0648\u0632\u064a\u0639\u0647\u0627 \u0628\u062a\u0628\u0627\u064a\u0646 \u0645\u062a\u0633\u0627\u0648\u064a \u0639\u0646\u062f \u0643\u0644 \u0645\u0633\u062a\u0648\u0649 \u0645\u0646 \u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u062a\u0648\u0642\u0639. \u064a\u064f\u0639\u0631\u0641 \u0647\u0630\u0627 \u0627\u0644\u0627\u0641\u062a\u0631\u0627\u0636 \u0628\u0627\u0644\u0645\u062b\u0644\u064a\u0629 \u0627\u0644\u062c\u0646\u0633\u064a\u0629<\/strong> .<\/span><\/p>\n

\u0639\u0646\u062f\u0645\u0627 \u0644\u0627 \u064a\u062a\u0645 \u0627\u062d\u062a\u0631\u0627\u0645 \u0647\u0630\u0627 \u0627\u0644\u0627\u0641\u062a\u0631\u0627\u0636\u060c \u064a\u0642\u0627\u0644 \u0623\u0646 \u0627\u0644\u062a\u063a\u0627\u064a\u0631\u064a\u0629<\/a> \u0645\u0648\u062c\u0648\u062f\u0629 \u0641\u064a \u0627\u0644\u0628\u0642\u0627\u064a\u0627. \u0639\u0646\u062f\u0645\u0627 \u064a\u062d\u062f\u062b \u0647\u0630\u0627\u060c \u062a\u0635\u0628\u062d \u0646\u062a\u0627\u0626\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u063a\u064a\u0631 \u0645\u0648\u062b\u0648\u0642\u0629.<\/span><\/p>\n

\u0625\u062d\u062f\u0649 \u0637\u0631\u0642 \u062d\u0644 \u0647\u0630\u0647 \u0627\u0644\u0645\u0634\u0643\u0644\u0629 \u0647\u064a \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0645\u0631\u0628\u0639\u0627\u062a \u0627\u0644\u0635\u063a\u0631\u0649 \u0627\u0644\u0645\u0631\u062c\u062d\u0629<\/strong> \u060c \u0648\u0627\u0644\u0630\u064a \u064a\u0639\u064a\u0646 \u0623\u0648\u0632\u0627\u0646\u064b\u0627 \u0644\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a<\/a> \u0628\u062d\u064a\u062b \u062a\u062a\u0644\u0642\u0649 \u062a\u0644\u0643 \u0630\u0627\u062a \u062a\u0628\u0627\u064a\u0646 \u0627\u0644\u062e\u0637\u0623 \u0627\u0644\u0645\u0646\u062e\u0641\u0636 \u0648\u0632\u0646\u064b\u0627 \u0623\u0643\u0628\u0631 \u0644\u0623\u0646\u0647\u0627 \u062a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 \u0645\u0639\u0644\u0648\u0645\u0627\u062a \u0623\u0643\u062b\u0631 \u0645\u0642\u0627\u0631\u0646\u0629 \u0628\u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0630\u0627\u062a \u062a\u0628\u0627\u064a\u0646 \u0627\u0644\u062e\u0637\u0623 \u0627\u0644\u0623\u0643\u0628\u0631.<\/span><\/p>\n

\u064a\u0642\u062f\u0645 \u0647\u0630\u0627 \u0627\u0644\u0628\u0631\u0646\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a \u0645\u062b\u0627\u0644\u0627\u064b \u062e\u0637\u0648\u0629 \u0628\u062e\u0637\u0648\u0629 \u0644\u0643\u064a\u0641\u064a\u0629 \u062a\u0646\u0641\u064a\u0630 \u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0645\u0631\u0628\u0639\u0627\u062a \u0627\u0644\u0635\u063a\u0631\u0649 \u0627\u0644\u0645\u0631\u062c\u062d\u0629 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646.<\/span><\/p>\n

\u0627\u0644\u062e\u0637\u0648\u0629 1: \u0625\u0646\u0634\u0627\u0621 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a<\/strong><\/span><\/h2>\n

\u0623\u0648\u0644\u0627\u064b\u060c \u0644\u0646\u0642\u0645 \u0628\u0625\u0646\u0634\u0627\u0621 \u0625\u0637\u0627\u0631 \u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0628\u0627\u0646\u062f\u0627 \u0627\u0644\u062a\u0627\u0644\u064a \u0627\u0644\u0630\u064a \u064a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 \u0645\u0639\u0644\u0648\u0645\u0627\u062a \u062d\u0648\u0644 \u0639\u062f\u062f \u0633\u0627\u0639\u0627\u062a \u0627\u0644\u062f\u0631\u0627\u0633\u0629 \u0648\u062f\u0631\u062c\u0629 \u0627\u0644\u0627\u062e\u062a\u0628\u0627\u0631 \u0627\u0644\u0646\u0647\u0627\u0626\u064a \u0644\u0640 16 \u0637\u0627\u0644\u0628\u064b\u0627 \u0641\u064a \u0627\u0644\u0641\u0635\u0644:<\/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
 \u0627\u0644\u062e\u0637\u0648\u0629 2: \u062a\u0646\u0627\u0633\u0628 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0627\u0644\u0628\u0633\u064a\u0637<\/strong><\/span><\/h2>\n
 \u0628\u0639\u062f \u0630\u0644\u0643\u060c \u0633\u0648\u0641 \u0646\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u0648\u0638\u0627\u0626\u0641 \u0627\u0644\u0645\u0648\u062c\u0648\u062f\u0629 \u0641\u064a \u0648\u062d\u062f\u0629 statsmodels<\/strong> \u0644\u062a\u0646\u0627\u0633\u0628 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0627\u0644\u0628\u0633\u064a\u0637 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0633\u0627\u0639\u0627\u062a<\/strong> \u0643\u0645\u062a\u063a\u064a\u0631 \u0645\u062a\u0648\u0642\u0639 \u0648\u0627\u0644\u0646\u062a\u064a\u062c\u0629<\/strong> \u0643\u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629:<\/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
 \u0645\u0646 \u0645\u0644\u062e\u0635 \u0627\u0644\u0646\u0645\u0648\u0630\u062c\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0631\u0649 \u0623\u0646 \u0642\u064a\u0645\u0629 R \u0644\u0644\u0646\u0645\u0648\u0630\u062c \u0647\u064a 0.630<\/strong> .<\/span><\/p>\n
 \u0627\u0644\u062e\u0637\u0648\u0629 3: \u062a\u0646\u0627\u0633\u0628 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0645\u0631\u0628\u0639\u0627\u062a \u0627\u0644\u0635\u063a\u0631\u0649 \u0627\u0644\u0645\u0631\u062c\u062d\u0629<\/strong><\/span><\/h2>\n
 \u0628\u0639\u062f \u0630\u0644\u0643\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u0629 statsmodels<\/strong> WLS()<\/strong> \u0644\u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0645\u0631\u0628\u0639\u0627\u062a \u0627\u0644\u0635\u063a\u0631\u0649 \u0627\u0644\u0645\u0631\u062c\u062d\u0629 \u0639\u0646 \u0637\u0631\u064a\u0642 \u062a\u0639\u064a\u064a\u0646 \u0627\u0644\u0623\u0648\u0632\u0627\u0646 \u0628\u062d\u064a\u062b \u062a\u062a\u0644\u0642\u0649 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0630\u0627\u062a \u0627\u0644\u062a\u0628\u0627\u064a\u0646 \u0627\u0644\u0623\u0642\u0644 \u0648\u0632\u0646\u064b\u0627 \u0623\u0643\u0628\u0631:<\/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
 \u0645\u0646 \u0627\u0644\u0646\u062a\u064a\u062c\u0629\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0631\u0649 \u0623\u0646 \u0642\u064a\u0645\u0629 R-squared \u0644\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0645\u0631\u0628\u0639\u0627\u062a \u0627\u0644\u0635\u063a\u0631\u0649 \u0627\u0644\u0645\u0648\u0632\u0648\u0646\u0629 \u0647\u0630\u0627 \u0642\u062f \u0632\u0627\u062f\u062a \u0625\u0644\u0649 0.676<\/strong> .<\/span><\/p>\n
 \u064a\u0634\u064a\u0631 \u0647\u0630\u0627 \u0625\u0644\u0649 \u0623\u0646 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0645\u0631\u0628\u0639\u0627\u062a \u0627\u0644\u0635\u063a\u0631\u0649 \u0627\u0644\u0645\u0631\u062c\u062d\u0629 \u0642\u0627\u062f\u0631 \u0639\u0644\u0649 \u062a\u0641\u0633\u064a\u0631 \u0627\u0644\u062a\u0628\u0627\u064a\u0646 \u0641\u064a \u062f\u0631\u062c\u0627\u062a \u0627\u0644\u0627\u0645\u062a\u062d\u0627\u0646\u0627\u062a \u0623\u0643\u062b\u0631 \u0645\u0646 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0627\u0644\u0628\u0633\u064a\u0637.<\/span><\/p>\n
 \u064a\u062e\u0628\u0631\u0646\u0627 \u0647\u0630\u0627 \u0623\u0646 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0645\u0631\u0628\u0639\u0627\u062a \u0627\u0644\u0635\u063a\u0631\u0649 \u0627\u0644\u0645\u0648\u0632\u0648\u0646\u0629 \u064a\u0648\u0641\u0631 \u0645\u0644\u0627\u0621\u0645\u0629 \u0623\u0641\u0636\u0644 \u0644\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0645\u0642\u0627\u0631\u0646\u0629 \u0628\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0627\u0644\u0628\u0633\u064a\u0637.<\/span><\/p>\n
 \u0645\u0635\u0627\u062f\u0631 \u0625\u0636\u0627\u0641\u064a\u0629<\/strong><\/span><\/h2>\n
 \u062a\u0634\u0631\u062d \u0627\u0644\u0628\u0631\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u062a\u0646\u0641\u064a\u0630 \u0627\u0644\u0645\u0647\u0627\u0645 \u0627\u0644\u0634\u0627\u0626\u0639\u0629 \u0627\u0644\u0623\u062e\u0631\u0649 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646:<\/span><\/p>\n
 \u0643\u064a\u0641\u064a\u0629 \u0625\u0646\u0634\u0627\u0621 \u0645\u0624\u0627\u0645\u0631\u0629 \u0627\u0644\u0645\u062a\u0628\u0642\u064a\u0629 \u0641\u064a \u0628\u064a\u062b\u0648\u0646<\/a>
 \u0643\u064a\u0641\u064a\u0629 \u0625\u0646\u0634\u0627\u0621 \u0645\u0624\u0627\u0645\u0631\u0629 QQ \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646<\/a>
 \u0643\u064a\u0641\u064a\u0629 \u0627\u062e\u062a\u0628\u0627\u0631 \u0627\u0644\u062e\u0637\u064a\u0629 \u0627\u0644\u0645\u062a\u0639\u062f\u062f\u0629 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
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