{"id":3626,"date":"2023-07-16T11:24:36","date_gmt":"2023-07-16T11:24:36","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d8%a7%d8%b3%d8%aa%d8%ae%d8%b1%d8%ac-%d9%85%d8%b1%d8%a8%d8%b9-r-%d9%85%d9%86-lm-%d8%a7%d9%95%d9%84%d9%89-r\/"},"modified":"2023-07-16T11:24:36","modified_gmt":"2023-07-16T11:24:36","slug":"%d8%a7%d8%b3%d8%aa%d8%ae%d8%b1%d8%ac-%d9%85%d8%b1%d8%a8%d8%b9-r-%d9%85%d9%86-lm-%d8%a7%d9%95%d9%84%d9%89-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/%d8%a7%d8%b3%d8%aa%d8%ae%d8%b1%d8%ac-%d9%85%d8%b1%d8%a8%d8%b9-r-%d9%85%d9%86-lm-%d8%a7%d9%95%d9%84%d9%89-r\/","title":{"rendered":"\u0643\u064a\u0641\u064a\u0629 \u0627\u0633\u062a\u062e\u0631\u0627\u062c r-squared \u0645\u0646 \u0648\u0638\u064a\u0641\u0629 lm() \u0641\u064a r"},"content":{"rendered":"

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\u064a\u0645\u0643\u0646\u0643 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0635\u064a\u063a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0644\u0627\u0633\u062a\u062e\u0631\u0627\u062c \u0642\u064a\u0645 R-squared<\/a> \u0648\u0642\u064a\u0645 R-squared \u0627\u0644\u0645\u0639\u062f\u0644\u0629<\/a> \u0645\u0646 \u0627\u0644\u062f\u0627\u0644\u0629 lm()<\/a> \u0641\u064a R:<\/span><\/p>\n

 #extract R-squared\nsummary(model)$adj.r.squared<\/span>\n\n#extract adjusted R-squared<\/span>\nsummary(model)$adj.r.squared\n<\/strong><\/pre>\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: \u0627\u0633\u062a\u062e\u0631\u0627\u062c R-Squared \u0645\u0646 lm() \u0641\u064a R<\/strong><\/span><\/h2>\n
 \u0644\u0646\u0641\u062a\u0631\u0636 \u0623\u0646\u0646\u0627 \u0646\u0644\u0627\u0626\u0645 \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 \u0627\u0644\u062a\u0627\u0644\u064a \u0641\u064a R:<\/span><\/p>\n
 #create data frame\n<\/span>df <- data. frame<\/span> (rating=c(67, 75, 79, 85, 90, 96, 97),\n                 points=c(8, 12, 16, 15, 22, 28, 24),\n                 assists=c(4, 6, 6, 5, 3, 8, 7),\n                 rebounds=c(1, 4, 3, 3, 2, 6, 7))\n\n#fit multiple linear regression model\n<\/span>model <- lm(rating ~ points + assists + rebounds, data=df)\n<\/strong><\/pre>\n
 \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u0629 Summary()<\/strong> \u0644\u0639\u0631\u0636 \u0627\u0644\u0645\u0644\u062e\u0635 \u0627\u0644\u0643\u0627\u0645\u0644 \u0644\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631:<\/span><\/p>\n
 #view model summary\n<\/span>summary(model)\n\nCall:\nlm(formula = rating ~ points + assists + rebounds, data = df)\n\nResiduals:\n      1 2 3 4 5 6 7 \n-1.5902 -1.7181 0.2413 4.8597 -1.0201 -0.6082 -0.1644 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)   \n(Intercept) 66.4355 6.6932 9.926 0.00218 **\npoints 1.2152 0.2788 4.359 0.02232 * \nassists -2.5968 1.6263 -1.597 0.20860   \nrebounds 2.8202 1.6118 1.750 0.17847   \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 3.193 on 3 degrees of freedom\nMultiple R-squared: 0.9589, Adjusted R-squared: 0.9179 \nF-statistic: 23.35 on 3 and 3 DF, p-value: 0.01396\n<\/strong><\/pre>\n
 \u0644\u0627\u062d\u0638 \u0642\u064a\u0645 R-squared \u0648R-squared \u0627\u0644\u0645\u0639\u062f\u0644\u0629 \u0644\u0644\u0646\u0645\u0648\u0630\u062c \u0628\u0627\u0644\u0642\u0631\u0628 \u0645\u0646 \u0623\u0633\u0641\u0644 \u0627\u0644\u0625\u062e\u0631\u0627\u062c:<\/span><\/p>\n