{"id":3571,"date":"2023-07-16T18:41:15","date_gmt":"2023-07-16T18:41:15","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d8%aa%d8%aa%d9%86%d8%a8%d8%a7%d9%94-%d9%86%d9%85%d8%a7%d8%b0%d8%ac-%d8%a7%d9%84%d8%a7%d9%95%d8%ad%d8%b5%d8%a7%d9%8a%d9%94%d9%8a%d8%a7%d8%aa\/"},"modified":"2023-07-16T18:41:15","modified_gmt":"2023-07-16T18:41:15","slug":"%d8%aa%d8%aa%d9%86%d8%a8%d8%a7%d9%94-%d9%86%d9%85%d8%a7%d8%b0%d8%ac-%d8%a7%d9%84%d8%a7%d9%95%d8%ad%d8%b5%d8%a7%d9%8a%d9%94%d9%8a%d8%a7%d8%aa","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/%d8%aa%d8%aa%d9%86%d8%a8%d8%a7%d9%94-%d9%86%d9%85%d8%a7%d8%b0%d8%ac-%d8%a7%d9%84%d8%a7%d9%95%d8%ad%d8%b5%d8%a7%d9%8a%d9%94%d9%8a%d8%a7%d8%aa\/","title":{"rendered":"\u0643\u064a\u0641\u064a\u0629 \u0639\u0645\u0644 \u062a\u0646\u0628\u0624\u0627\u062a \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0641\u064a statsmodels"},"content":{"rendered":"

<\/p>\n

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\u064a\u0645\u0643\u0646\u0643 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0635\u064a\u063a\u0629 \u0627\u0644\u0623\u0633\u0627\u0633\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0644\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0645\u0644\u0627\u0621\u0645\u0629 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0648\u062d\u062f\u0629 statsmodels<\/a> \u0641\u064a Python \u0644\u0625\u062c\u0631\u0627\u0621 \u062a\u0646\u0628\u0624\u0627\u062a \u062d\u0648\u0644 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0627\u0644\u062c\u062f\u064a\u062f\u0629:<\/span><\/p>\n

 model. predict<\/span> (df_new)\n<\/strong><\/pre>\n
 \u0633\u064a\u062d\u0633\u0628 \u0628\u0646\u0627\u0621 \u0627\u0644\u062c\u0645\u0644\u0629 \u0647\u0630\u0627 \u0642\u064a\u0645 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629 \u0627\u0644\u0645\u062a\u0648\u0642\u0639\u0629 \u0644\u0643\u0644 \u0635\u0641 \u0645\u0646 DataFrame \u062c\u062f\u064a\u062f \u064a\u0633\u0645\u0649 df_new<\/strong> \u060c \u0648\u0630\u0644\u0643 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0646\u0645\u0648\u0630\u062c \u0627\u0646\u062d\u062f\u0627\u0631 \u0645\u0646\u0627\u0633\u0628 \u0644\u0644\u0646\u0645\u0627\u0630\u062c \u0627\u0644\u0625\u062d\u0635\u0627\u0626\u064a\u0629 \u0627\u0644\u062a\u064a \u062a\u0633\u0645\u0649 model<\/strong> .<\/span><\/p>\n
 \u064a\u0648\u0636\u062d \u0627\u0644\u0645\u062b\u0627\u0644 \u0627\u0644\u062a\u0627\u0644\u064a \u0643\u064a\u0641\u064a\u0629 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0628\u0646\u0627\u0621 \u0627\u0644\u062c\u0645\u0644\u0629 \u0647\u0630\u0627 \u0639\u0645\u0644\u064a\u064b\u0627.<\/span><\/p>\n
 \u0645\u062b\u0627\u0644: \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u062a\u0646\u0628\u0624\u0627\u062a \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0641\u064a Statsmodels<\/strong><\/span><\/h2>\n
 \u0644\u0646\u0641\u062a\u0631\u0636 \u0623\u0646 \u0644\u062f\u064a\u0646\u0627 DataFrame \u0627\u0644\u0628\u0627\u0646\u062f\u0627 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0627\u0644\u062a\u064a \u062a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 \u0645\u0639\u0644\u0648\u0645\u0627\u062a \u062d\u0648\u0644 \u0633\u0627\u0639\u0627\u062a \u0627\u0644\u062f\u0631\u0627\u0633\u0629 \u0648\u0627\u0644\u0627\u0645\u062a\u062d\u0627\u0646\u0627\u062a \u0627\u0644\u062a\u062d\u0636\u064a\u0631\u064a\u0629 \u0627\u0644\u062a\u064a \u062a\u0645 \u0625\u062c\u0631\u0627\u0624\u0647\u0627 \u0648\u0627\u0644\u062f\u0631\u062c\u0629 \u0627\u0644\u0646\u0647\u0627\u0626\u064a\u0629 \u0627\u0644\u062a\u064a \u062d\u0635\u0644 \u0639\u0644\u064a\u0647\u0627 \u0627\u0644\u0637\u0644\u0627\u0628 \u0641\u064a \u0641\u0635\u0644 \u0645\u0639\u064a\u0646:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' hours<\/span> ': [1, 2, 2, 4, 2, 1, 5, 4, 2, 4, 4, 3, 6],\n                   ' exams<\/span> ': [1, 3, 3, 5, 2, 2, 1, 1, 0, 3, 4, 3, 2],\n                   ' score<\/span> ': [76, 78, 85, 88, 72, 69, 94, 94, 88, 92, 90, 75, 96]})\n\n#view head of DataFrame\n<\/span>df. head<\/span> ()\n\n\thours exam score\n0 1 1 76\n1 2 3 78\n2 2 3 85\n3 4 5 88\n4 2 2 72<\/strong><\/pre>\n
 import<\/span> statsmodels. api<\/span> as<\/span> sm\n\n#define predictor and response variables\n<\/span>y = df[' score<\/span> ']\nx = df[[' hours<\/span> ', ' exams<\/span> ']]\n\n#add constant to predictor variables\n<\/span>x = sm. add_constant<\/span> (x)\n\n#fit linear regression model\n<\/span>model = sm. OLS<\/span> (y,x). fit<\/span> ()\n\n#view model summary\n<\/span>print<\/span> ( model.summary<\/span> ())\n\n                            OLS Regression Results                            \n==================================================== ============================\nDept. Variable: R-squared score: 0.718\nModel: OLS Adj. R-squared: 0.661\nMethod: Least Squares F-statistic: 12.70\nDate: Fri, 05 Aug 2022 Prob (F-statistic): 0.00180\nTime: 09:24:38 Log-Likelihood: -38.618\nNo. Observations: 13 AIC: 83.24\nDf Residuals: 10 BIC: 84.93\nDf Model: 2                                         \nCovariance Type: non-robust                                         \n==================================================== ============================\n                 coef std err t P>|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 71.4048 4.001 17.847 0.000 62.490 80.319\nhours 5.1275 1.018 5.038 0.001 2.860 7.395\nexams -1.2121 1.147 -1.057 0.315 -3.768 1.344\n==================================================== ============================\nOmnibus: 1,103 Durbin-Watson: 1,248\nProb(Omnibus): 0.576 Jarque-Bera (JB): 0.803\nSkew: -0.289 Prob(JB): 0.669\nKurtosis: 1.928 Cond. No. 11.7\n==================================================== ============================\n<\/strong><\/pre>\n
 \u0645\u0646 \u0639\u0645\u0648\u062f coef<\/strong> \u0641\u064a \u0627\u0644\u0645\u062e\u0631\u062c\u0627\u062a\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0643\u062a\u0627\u0628\u0629 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0645\u0646\u0627\u0633\u0628:<\/span><\/p>\n
 \u0627\u0644\u0646\u062a\u064a\u062c\u0629 = 71.4048 + 5.1275 (\u0633\u0627\u0639\u0627\u062a) \u2013 1.2121 (\u0627\u0645\u062a\u062d\u0627\u0646\u0627\u062a)<\/span><\/p>\n
 \u0644\u0646\u0641\u062a\u0631\u0636 \u0627\u0644\u0622\u0646 \u0623\u0646\u0646\u0627 \u0646\u0631\u064a\u062f \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0645\u0646\u0627\u0633\u0628 \u0644\u0644\u062a\u0646\u0628\u0624 \u0628\u0640 “\u062f\u0631\u062c\u0629” \u062e\u0645\u0633\u0629 \u0637\u0644\u0627\u0628 \u062c\u062f\u062f.<\/span><\/p>\n
 \u0623\u0648\u0644\u0627\u064b\u060c \u0644\u0646\u0642\u0645 \u0628\u0625\u0646\u0634\u0627\u0621 DataFrame \u0644\u0627\u0633\u062a\u064a\u0639\u0627\u0628 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0627\u0644\u062e\u0645\u0633 \u0627\u0644\u062c\u062f\u064a\u062f\u0629:<\/span><\/p>\n
 #create new DataFrame\ndf_new = pd. DataFrame<\/span> ({' hours<\/span> ': [1, 2, 2, 4, 5],\n                       ' exams<\/span> ': [1, 1, 4, 3, 3]})<\/span>\n\n#add column for constant\ndf_new = sm. add_constant<\/span> (df_new)\n<\/span>\n#view new DataFrame\nprint<\/span> (df_new)\n\n   const hours exams\n0 1.0 1 1\n1 1.0 2 1\n2 1.0 2 4\n3 1.0 4 3\n4 1.0 5 3<\/span><\/span><\/strong><\/pre>\n
 \u0628\u0639\u062f \u0630\u0644\u0643\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u062f\u0627\u0644\u0629 \u0627\u0644\u062a\u0646\u0628\u0624 ()<\/strong> \u0644\u0644\u062a\u0646\u0628\u0624 \u0628\u0640 “\u0627\u0644\u0646\u062a\u064a\u062c\u0629” \u0644\u0643\u0644 \u0637\u0627\u0644\u0628 \u0645\u0646 \u0647\u0624\u0644\u0627\u0621 \u0627\u0644\u0637\u0644\u0627\u0628\u060c \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 “\u0627\u0644\u0633\u0627\u0639\u0627\u062a” \u0648”\u0627\u0644\u0627\u0645\u062a\u062d\u0627\u0646\u0627\u062a” \u0643\u0642\u064a\u0645 \u0644\u0645\u062a\u063a\u064a\u0631\u0627\u062a \u0627\u0644\u062a\u0648\u0642\u0639 \u0641\u064a \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0645\u0644\u0627\u0626\u0645 \u0644\u062f\u064a\u0646\u0627:<\/span><\/span><\/p>\n
 #predict scores for the five new students<\/span>\nmodel. predict<\/span> (df_new)\n\n0 75.320242\n1 80.447734\n2 76.811480\n3 88.278550\n4 93.406042\ndtype:float64\n<\/strong><\/pre>\n
 \u0648\u0625\u0644\u064a\u0643 \u0643\u064a\u0641\u064a\u0629 \u062a\u0641\u0633\u064a\u0631 \u0627\u0644\u0646\u062a\u064a\u062c\u0629:<\/span><\/p>\n
 \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u0629 OLS()<\/strong> \u200e \u0641\u064a \u0648\u062d\u062f\u0629 statsmodels \u0644\u0645\u0644\u0627\u0621\u0645\u0629 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0627\u0644\u0645\u062a\u0639\u062f\u062f<\/a> \u060c \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 “\u0627\u0644\u0633\u0627\u0639\u0627\u062a” \u0648”\u0627\u0644\u0627\u0645\u062a\u062d\u0627\u0646\u0627\u062a” \u0643\u0645\u062a\u063a\u064a\u0631\u0627\u062a \u0644\u0644\u062a\u0646\u0628\u0624 \u0648”\u0627\u0644\u0646\u062a\u064a\u062c\u0629” \u0643\u0645\u062a\u063a\u064a\u0631 \u0644\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629:<\/span><\/p>\n