{"id":781,"date":"2023-07-28T18:44:38","date_gmt":"2023-07-28T18:44:38","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d8%a7%d9%86%d8%ad%d8%af%d8%a7%d8%b1-%d8%a7%d9%84%d8%aa%d8%b1%d8%a8%d9%8a%d8%b9%d9%8a-%d8%b5\/"},"modified":"2023-07-28T18:44:38","modified_gmt":"2023-07-28T18:44:38","slug":"%d8%a7%d9%84%d8%a7%d9%86%d8%ad%d8%af%d8%a7%d8%b1-%d8%a7%d9%84%d8%aa%d8%b1%d8%a8%d9%8a%d8%b9%d9%8a-%d8%b5","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d8%a7%d9%86%d8%ad%d8%af%d8%a7%d8%b1-%d8%a7%d9%84%d8%aa%d8%b1%d8%a8%d9%8a%d8%b9%d9%8a-%d8%b5\/","title":{"rendered":"\u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062a\u0631\u0628\u064a\u0639\u064a \u0641\u064a r"},"content":{"rendered":"

<\/p>\n

\n

\u0639\u0646\u062f\u0645\u0627 \u064a\u0643\u0648\u0646 \u0628\u064a\u0646 \u0645\u062a\u063a\u064a\u0631\u064a\u0646 \u0639\u0644\u0627\u0642\u0629 \u062e\u0637\u064a\u0629\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u063a\u0627\u0644\u0628\u064b\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0627\u0644\u0628\u0633\u064a\u0637<\/a> \u0644\u062a\u062d\u062f\u064a\u062f \u0627\u0644\u0639\u0644\u0627\u0642\u0629 \u0628\u064a\u0646\u0647\u0645\u0627.<\/span> <\/p>\n

\"\u0645\u062b\u0627\u0644<\/p>\n

\u0648\u0645\u0639 \u0630\u0644\u0643\u060c \u0639\u0646\u062f\u0645\u0627 \u064a\u0643\u0648\u0646 \u0644\u0645\u062a\u063a\u064a\u0631\u064a\u0646 \u0639\u0644\u0627\u0642\u0629 \u062a\u0631\u0628\u064a\u0639\u064a\u0629\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062a\u0631\u0628\u064a\u0639\u064a<\/strong> \u0644\u062a\u062d\u062f\u064a\u062f \u0627\u0644\u0639\u0644\u0627\u0642\u0629 \u0628\u064a\u0646\u0647\u0645\u0627.<\/span> <\/p>\n

\"\u0645\u062b\u0627\u0644<\/p>\n

\u064a\u0634\u0631\u062d \u0647\u0630\u0627 \u0627\u0644\u0628\u0631\u0646\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a \u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062a\u0631\u0628\u064a\u0639\u064a \u0641\u064a R.<\/span><\/p>\n

\u0645\u062b\u0627\u0644: \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062a\u0631\u0628\u064a\u0639\u064a \u0641\u064a R<\/strong><\/h3>\n

\u0644\u0646\u0641\u062a\u0631\u0636 \u0623\u0646\u0646\u0627 \u0646\u0631\u064a\u062f \u0623\u0646 \u0646\u0641\u0647\u0645 \u0627\u0644\u0639\u0644\u0627\u0642\u0629 \u0628\u064a\u0646 \u0639\u062f\u062f \u0633\u0627\u0639\u0627\u062a \u0627\u0644\u0639\u0645\u0644 \u0648\u0627\u0644\u0633\u0639\u0627\u062f\u0629 \u0627\u0644\u0645\u0628\u0644\u063a \u0639\u0646\u0647\u0627. \u0644\u062f\u064a\u0646\u0627 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0639\u0646 \u0639\u062f\u062f \u0633\u0627\u0639\u0627\u062a \u0627\u0644\u0639\u0645\u0644 \u0623\u0633\u0628\u0648\u0639\u064a\u064b\u0627 \u0648\u0645\u0633\u062a\u0648\u0649 \u0627\u0644\u0633\u0639\u0627\u062f\u0629 \u0627\u0644\u0645\u064f\u0628\u0644\u063a \u0639\u0646\u0647 (\u0639\u0644\u0649 \u0645\u0642\u064a\u0627\u0633 \u0645\u0646 0 \u0625\u0644\u0649 100) \u0644\u0640 11 \u0634\u062e\u0635\u064b\u0627 \u0645\u062e\u062a\u0644\u0641\u064b\u0627:<\/span> <\/p>\n

\"\"<\/p>\n

\u0627\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u062e\u0637\u0648\u0627\u062a \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0644\u062a\u0646\u0627\u0633\u0628 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062a\u0631\u0628\u064a\u0639\u064a \u0641\u064a R.<\/span><\/p>\n

\u0627\u0644\u062e\u0637\u0648\u0629 1: \u0623\u062f\u062e\u0644 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a.<\/strong><\/span><\/p>\n

\u0623\u0648\u0644\u0627\u064b\u060c \u0633\u0646\u0642\u0648\u0645 \u0628\u0625\u0646\u0634\u0627\u0621 \u0625\u0637\u0627\u0631 \u0628\u064a\u0627\u0646\u0627\u062a \u064a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 \u0628\u064a\u0627\u0646\u0627\u062a\u0646\u0627:<\/span><\/p>\n

 #createdata<\/span>\ndata <- data.frame(hours=c(6, 9, 12, 14, 30, 35, 40, 47, 51, 55, 60),\n                   happiness=c(14, 28, 50, 70, 89, 94, 90, 75, 59, 44, 27))\n\n#viewdata<\/span> \ndata\n\n   hours happiness\n1 6 14\n2 9 28\n3 12 50\n4 14 70\n5 30 89\n6 35 94\n7 40 90\n8 47 75\n9 51 59\n10 55 44\n11 60 27\n<\/strong><\/pre>\n
 \u0627\u0644\u062e\u0637\u0648\u0629 2: \u062a\u0635\u0648\u0631 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a.<\/strong><\/span><\/p>\n
 \u0628\u0639\u062f \u0630\u0644\u0643\u060c \u0633\u0648\u0641 \u0646\u0642\u0648\u0645 \u0628\u0625\u0646\u0634\u0627\u0621 \u0645\u062e\u0637\u0637 \u062a\u0634\u062a\u062a \u0628\u0633\u064a\u0637 \u0644\u062a\u0635\u0648\u0631 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a.<\/span> <\/p>\n
 #create scatterplot<\/span>\nplot(data$hours, data$happiness, pch=16)<\/strong> <\/pre>\n
\"\u0645\u062e\u0637\u0637<\/p>\n
 \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0631\u0649 \u0628\u0648\u0636\u0648\u062d \u0623\u0646 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0644\u0627 \u062a\u062a\u0628\u0639 \u0646\u0645\u0637\u064b\u0627 \u062e\u0637\u064a\u064b\u0627.<\/span><\/p>\n
 \u0627\u0644\u062e\u0637\u0648\u0629 3: \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><\/p>\n
 \u0628\u0639\u062f \u0630\u0644\u0643\u060c \u0633\u0646\u0642\u0648\u0645 \u0628\u062a\u0631\u0643\u064a\u0628 \u0646\u0645\u0648\u0630\u062c \u0627\u0646\u062d\u062f\u0627\u0631 \u062e\u0637\u064a \u0628\u0633\u064a\u0637 \u0644\u0645\u0639\u0631\u0641\u0629 \u0645\u062f\u0649 \u0645\u0644\u0627\u0621\u0645\u062a\u0647 \u0644\u0644\u0628\u064a\u0627\u0646\u0627\u062a:<\/span><\/p>\n
 #fit linear model<\/span>\nlinearModel <- lm(happiness ~ hours, data=data)\n\n#view model summary<\/span>\nsummary(linearModel)\n\nCall:\nlm(formula = happiness ~ hours)\n\nResiduals:\n   Min 1Q Median 3Q Max \n-39.34 -21.99 -2.03 23.50 35.11 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)  \n(Intercept) 48.4531 17.3288 2.796 0.0208 *\nhours 0.2981 0.4599 0.648 0.5331  \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 28.72 on 9 degrees of freedom\nMultiple R-squared: 0.0446, Adjusted R-squared: -0.06156 \nF-statistic: 0.4201 on 1 and 9 DF, p-value: 0.5331\n<\/strong><\/pre>\n
 \u0625\u062c\u0645\u0627\u0644\u064a \u0627\u0644\u062a\u0628\u0627\u064a\u0646 \u0641\u064a \u0627\u0644\u0633\u0639\u0627\u062f\u0629 \u0627\u0644\u0630\u064a \u0623\u0648\u0636\u062d\u0647 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0647\u0648 4.46%<\/strong> \u0641\u0642\u0637\u060c \u0643\u0645\u0627 \u0647\u0648 \u0645\u0648\u0636\u062d \u0645\u0646 \u062e\u0644\u0627\u0644 \u0642\u064a\u0645\u0629 R-squared \u0627\u0644\u0645\u062a\u0639\u062f\u062f\u0629.<\/span><\/p>\n
 \u0627\u0644\u062e\u0637\u0648\u0629 4: \u062a\u0646\u0627\u0633\u0628 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062a\u0631\u0628\u064a\u0639\u064a.<\/strong><\/span><\/p>\n
 \u0628\u0639\u062f \u0630\u0644\u0643\u060c \u0633\u0648\u0641 \u0646\u0642\u0648\u0645 \u0628\u062a\u0631\u0643\u064a\u0628 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062a\u0631\u0628\u064a\u0639\u064a.<\/span><\/p>\n
 #create a new variable for hours 2<\/sup><\/span>\ndata$hours2 <- data$hours^2\n\n#fit quadratic regression model<\/span>\nquadraticModel <- lm(happiness ~ hours + hours2, data=data)\n\n#view model summary\n<\/span>summary(quadraticModel)\n\nCall:\nlm(formula = happiness ~ hours + hours2, data = data)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-6.2484 -3.7429 -0.1812 1.1464 13.6678 \n\nCoefficients:\n             Estimate Std. Error t value Pr(>|t|)    \n(Intercept) -18.25364 6.18507 -2.951 0.0184 *  \nhours 6.74436 0.48551 13.891 6.98e-07 ***\nhours2 -0.10120 0.00746 -13.565 8.38e-07 ***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 6.218 on 8 degrees of freedom\nMultiple R-squared: 0.9602, Adjusted R-squared: 0.9502 \nF-statistic: 96.49 on 2 and 8 DF, p-value: 2.51e-06\n<\/strong><\/pre>\n
 \u0648\u0627\u0631\u062a\u0641\u0639 \u0627\u0644\u062a\u0628\u0627\u064a\u0646 \u0627\u0644\u0643\u0644\u064a \u0641\u064a \u0627\u0644\u0633\u0639\u0627\u062f\u0629 \u0627\u0644\u0645\u0641\u0633\u0631 \u0628\u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0625\u0644\u0649 96.02%<\/b> .<\/span><\/p>\n
 \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0643\u0648\u062f \u0627\u0644\u062a\u0627\u0644\u064a \u0644\u062a\u0635\u0648\u0631 \u0645\u062f\u0649 \u0645\u0644\u0627\u0621\u0645\u0629 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0644\u0644\u0628\u064a\u0627\u0646\u0627\u062a:<\/span> <\/p>\n
 #create sequence of hour values<\/span>\nhourValues <- seq(0, 60, 0.1)\n\n#create list of predicted happiness levels using quadratic model\n<\/span>happinessPredict <- predict(quadraticModel, list(hours=hourValues, hours2=hourValues^2))\n\n#create scatterplot of original data values\n<\/span>plot(data$hours, data$happiness, pch=16)\n#add predicted lines based on quadratic regression model<\/span>\nlines(hourValues, happinessPredict, col='blue')\n<\/strong><\/pre>\n
\"\u0645\u062e\u0637\u0637<\/p>\n
 \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0631\u0649 \u0623\u0646 \u062e\u0637 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062a\u0631\u0628\u064a\u0639\u064a \u064a\u0646\u0627\u0633\u0628 \u0642\u064a\u0645 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0628\u0634\u0643\u0644 \u062c\u064a\u062f.<\/span><\/p>\n
 \u0627\u0644\u062e\u0637\u0648\u0629 5: \u062a\u0641\u0633\u064a\u0631 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062a\u0631\u0628\u064a\u0639\u064a.<\/strong><\/span><\/p>\n
 \u0631\u0623\u064a\u0646\u0627 \u0641\u064a \u0627\u0644\u062e\u0637\u0648\u0629 \u0627\u0644\u0633\u0627\u0628\u0642\u0629 \u0623\u0646 \u0646\u062a\u064a\u062c\u0629 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062a\u0631\u0628\u064a\u0639\u064a \u0643\u0627\u0646\u062a:<\/span><\/p>\n
 Coefficients:\n             Estimate Std. Error t value Pr(>|t|)    \n(Intercept) -18.25364 6.18507 -2.951 0.0184 *  \nhours 6.74436 0.48551 13.891 6.98e-07 ***\nhours2 -0.10120 0.00746 -13.565 8.38e-07 ***\n<\/strong><\/pre>\n
 \u0628\u0646\u0627\u0621\u064b \u0639\u0644\u0649 \u0627\u0644\u0645\u0639\u0627\u0645\u0644\u0627\u062a \u0627\u0644\u0645\u0639\u0631\u0648\u0636\u0629 \u0647\u0646\u0627\u060c \u0633\u064a\u0643\u0648\u0646 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062a\u0631\u0628\u064a\u0639\u064a \u0627\u0644\u0645\u0639\u062f\u0644 \u0643\u0645\u0627 \u064a\u0644\u064a:<\/span><\/p>\n
 \u0627\u0644\u0633\u0639\u0627\u062f\u0629 = -0.1012 (\u0633\u0627\u0639\u0629) 2<\/sup> + 6.7444 (\u0633\u0627\u0639\u0629) \u2013 18.2536<\/strong><\/span><\/p>\n
 \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0647\u0630\u0647 \u0627\u0644\u0645\u0639\u0627\u062f\u0644\u0629 \u0644\u0644\u0639\u062b\u0648\u0631 \u0639\u0644\u0649 \u0627\u0644\u0633\u0639\u0627\u062f\u0629 \u0627\u0644\u0645\u062a\u0648\u0642\u0639\u0629 \u0644\u0644\u0641\u0631\u062f\u060c \u0628\u0627\u0644\u0646\u0638\u0631 \u0625\u0644\u0649 \u0639\u062f\u062f \u0627\u0644\u0633\u0627\u0639\u0627\u062a \u0627\u0644\u062a\u064a \u064a\u0639\u0645\u0644\u0647\u0627 \u0641\u064a \u0627\u0644\u0623\u0633\u0628\u0648\u0639.<\/span><\/p>\n
 \u0639\u0644\u0649 \u0633\u0628\u064a\u0644 \u0627\u0644\u0645\u062b\u0627\u0644\u060c \u0627\u0644\u0634\u062e\u0635 \u0627\u0644\u0630\u064a \u064a\u0639\u0645\u0644 60 \u0633\u0627\u0639\u0629 \u0641\u064a \u0627\u0644\u0623\u0633\u0628\u0648\u0639 \u0633\u064a\u0643\u0648\u0646 \u0644\u062f\u064a\u0647 \u0645\u0633\u062a\u0648\u0649 \u0633\u0639\u0627\u062f\u0629 \u064a\u0628\u0644\u063a 22.09<\/strong> :<\/span><\/p>\n
 \u0627\u0644\u0633\u0639\u0627\u062f\u0629 = -0.1012(60) 2<\/sup> + 6.7444(60) \u2013 18.2536 = 22.09<\/span><\/p>\n
 \u0648\u0639\u0644\u0649 \u0627\u0644\u0639\u0643\u0633 \u0645\u0646 \u0630\u0644\u0643\u060c \u0641\u0625\u0646 \u0627\u0644\u0634\u062e\u0635 \u0627\u0644\u0630\u064a \u064a\u0639\u0645\u0644 30 \u0633\u0627\u0639\u0629 \u0623\u0633\u0628\u0648\u0639\u064a\u064b\u0627 \u064a\u062c\u0628 \u0623\u0646 \u064a\u0643\u0648\u0646 \u0645\u0633\u062a\u0648\u0649 \u0633\u0639\u0627\u062f\u062a\u0647 92.99<\/strong> :<\/span><\/p>\n
 \u0627\u0644\u0633\u0639\u0627\u062f\u0629 = -0.1012(30) 2<\/sup> + 6.7444(30) \u2013 18.2536 = 92.99<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
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