{"id":1640,"date":"2023-07-25T13:26:30","date_gmt":"2023-07-25T13:26:30","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d8%aa%d8%b9%d8%af%d9%8a%d9%84-%d9%85%d9%86%d8%ad%d9%86%d9%89-r\/"},"modified":"2023-07-25T13:26:30","modified_gmt":"2023-07-25T13:26:30","slug":"%d8%aa%d8%b9%d8%af%d9%8a%d9%84-%d9%85%d9%86%d8%ad%d9%86%d9%89-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/%d8%aa%d8%b9%d8%af%d9%8a%d9%84-%d9%85%d9%86%d8%ad%d9%86%d9%89-r\/","title":{"rendered":"\u062a\u0631\u0643\u064a\u0628 \u0627\u0644\u0645\u0646\u062d\u0646\u0649 \u0641\u064a r (\u0645\u0639 \u0623\u0645\u062b\u0644\u0629)"},"content":{"rendered":"

<\/p>\n

\n

\u0641\u064a \u0643\u062b\u064a\u0631 \u0645\u0646 \u0627\u0644\u0623\u062d\u064a\u0627\u0646 \u0642\u062f \u062a\u0631\u063a\u0628 \u0641\u064a \u0627\u0644\u0639\u062b\u0648\u0631 \u0639\u0644\u0649 \u0627\u0644\u0645\u0639\u0627\u062f\u0644\u0629 \u0627\u0644\u062a\u064a \u062a\u0646\u0627\u0633\u0628 \u0645\u0646\u062d\u0646\u0649 R.<\/span> <\/p>\n

\"\u062a\u0631\u0643\u064a\u0628<\/p>\n

\u064a\u0634\u0631\u062d \u0627\u0644\u0645\u062b\u0627\u0644 \u0627\u0644\u062a\u0627\u0644\u064a \u062e\u0637\u0648\u0629 \u0628\u062e\u0637\u0648\u0629 \u0643\u064a\u0641\u064a\u0629 \u0645\u0644\u0627\u0621\u0645\u0629 \u0627\u0644\u0645\u0646\u062d\u0646\u064a\u0627\u062a \u0644\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0641\u064a R \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u0629 poly()<\/strong> \u0648\u0643\u064a\u0641\u064a\u0629 \u062a\u062d\u062f\u064a\u062f \u0627\u0644\u0645\u0646\u062d\u0646\u0649 \u0627\u0644\u0630\u064a \u064a\u0646\u0627\u0633\u0628 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0628\u0634\u0643\u0644 \u0623\u0641\u0636\u0644.<\/span><\/p>\n

\u0627\u0644\u062e\u0637\u0648\u0629 1: \u0625\u0646\u0634\u0627\u0621 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0648\u062a\u0635\u0648\u0631\u0647\u0627<\/strong><\/span><\/h3>\n

\u0644\u0646\u0628\u062f\u0623 \u0628\u0625\u0646\u0634\u0627\u0621 \u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a \u0645\u0632\u064a\u0641\u0629\u060c \u062b\u0645 \u0646\u0646\u0634\u0626 \u0645\u062e\u0637\u0637\u064b\u0627 \u0645\u0628\u0639\u062b\u0631\u064b\u0627 \u0644\u062a\u0635\u0648\u0631 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a:<\/span> <\/p>\n

 #create data frame\n<\/span>df <- data. frame<\/span> (x=1:15,\n                 y=c(3, 14, 23, 25, 23, 15, 9, 5, 9, 13, 17, 24, 32, 36, 46))\n\n#create a scatterplot of x vs. y\n<\/span>plot(df$x, df$y, pch= 19<\/span> , xlab=' x<\/span> ', ylab=' y<\/span> ')<\/strong> <\/pre>\n
\"\"<\/p>\n
 \u0627\u0644\u062e\u0637\u0648\u0629 2: \u0636\u0628\u0637 \u0645\u0646\u062d\u0646\u064a\u0627\u062a \u0645\u062a\u0639\u062f\u062f\u0629<\/strong><\/span><\/h3>\n
 \u062f\u0639\u0648\u0646\u0627 \u0628\u0639\u062f \u0630\u0644\u0643 \u0646\u0644\u0627\u0626\u0645 \u0639\u062f\u0629 \u0646\u0645\u0627\u0630\u062c \u0627\u0646\u062d\u062f\u0627\u0631 \u0645\u062a\u0639\u062f\u062f\u0629 \u0627\u0644\u062d\u062f\u0648\u062f \u0645\u0639 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0648\u0646\u062a\u0635\u0648\u0631 \u0645\u0646\u062d\u0646\u0649 \u0643\u0644 \u0646\u0645\u0648\u0630\u062c \u0641\u064a \u0646\u0641\u0633 \u0627\u0644\u0645\u062e\u0637\u0637:<\/span> <\/p>\n
 #fit polynomial regression models up to degree 5\n<\/span>fit1 <- lm(y~x, data=df)\nfit2 <- lm(y~poly(x,2,raw= TRUE<\/span> ), data=df)\nfit3 <- lm(y~poly(x,3,raw= TRUE<\/span> ), data=df)\nfit4 <- lm(y~poly(x,4,raw= TRUE<\/span> ), data=df)\nfit5 <- lm(y~poly(x,5,raw= TRUE<\/span> ), data=df)<\/span>\n\n#create a scatterplot of x vs. y\n<\/span>plot(df$x, df$y, pch=19, xlab=' x<\/span> ', ylab=' y<\/span> ')\n\n#define x-axis values\n<\/span>x_axis <- seq(1, 15, length= 15<\/span> )\n\n#add curve of each model to plot\n<\/span>lines(x_axis, predict(fit1, data. frame<\/span> (x=x_axis)), col=' green<\/span> ')\nlines(x_axis, predict(fit2, data. frame<\/span> (x=x_axis)), col=' red<\/span> ')\nlines(x_axis, predict(fit3, data. frame<\/span> (x=x_axis)), col=' purple<\/span> ')\nlines(x_axis, predict(fit4, data. frame<\/span> (x=x_axis)), col=' blue<\/span> ')\nlines(x_axis, predict(fit5, data. frame<\/span> (x=x_axis)), col=' orange<\/span> ')\n<\/strong><\/pre>\n
\"\"<\/p>\n
 \u0644\u062a\u062d\u062f\u064a\u062f \u0627\u0644\u0645\u0646\u062d\u0646\u0649 \u0627\u0644\u0630\u064a \u064a\u0646\u0627\u0633\u0628 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0628\u0634\u0643\u0644 \u0623\u0641\u0636\u0644\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0646\u0638\u0631 \u0625\u0644\u0649 \u0645\u0631\u0628\u0639 R \u0627\u0644\u0645\u0639\u062f\u0644<\/a> \u0644\u0643\u0644 \u0646\u0645\u0648\u0630\u062c.<\/span><\/p>\n
 \u062a\u062e\u0628\u0631\u0646\u0627 \u0647\u0630\u0647 \u0627\u0644\u0642\u064a\u0645\u0629 \u0628\u0646\u0633\u0628\u0629 \u0627\u0644\u062a\u0628\u0627\u064a\u0646 \u0641\u064a \u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629 \u0627\u0644\u0630\u064a \u064a\u0645\u0643\u0646 \u062a\u0641\u0633\u064a\u0631\u0647 \u0628\u0648\u0627\u0633\u0637\u0629 \u0645\u062a\u063a\u064a\u0631 (\u0645\u062a\u063a\u064a\u0631\u0627\u062a) \u0627\u0644\u062a\u0648\u0642\u0639 \u0641\u064a \u0627\u0644\u0646\u0645\u0648\u0630\u062c\u060c \u0645\u0639 \u062a\u0639\u062f\u064a\u0644\u0647\u0627 \u0644\u0639\u062f\u062f \u0645\u062a\u063a\u064a\u0631\u0627\u062a \u0627\u0644\u062a\u0648\u0642\u0639.<\/span><\/p>\n
 #calculated adjusted R-squared of each model\n<\/span>summary(fit1)$adj. r<\/span> . squared<\/span>\nsummary(fit2)$adj. r<\/span> . squared<\/span>\nsummary(fit3)$adj. r<\/span> . squared<\/span>\nsummary(fit4)$adj. r<\/span> . squared<\/span>\nsummary(fit5)$adj. r<\/span> . squared<\/span>\n\n[1] 0.3144819\n[1] 0.5186706\n[1] 0.7842864\n[1] 0.9590276\n[1] 0.9549709\n<\/strong><\/pre>\n
 \u0645\u0646 \u0627\u0644\u0646\u062a\u064a\u062c\u0629\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0631\u0649 \u0623\u0646 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0630\u064a \u064a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 \u0623\u0639\u0644\u0649 \u0645\u0631\u0628\u0639 R \u0645\u0639\u062f\u0644 \u0647\u0648 \u0645\u062a\u0639\u062f\u062f \u0627\u0644\u062d\u062f\u0648\u062f \u0645\u0646 \u0627\u0644\u062f\u0631\u062c\u0629 \u0627\u0644\u0631\u0627\u0628\u0639\u0629\u060c \u0648\u0627\u0644\u0630\u064a \u064a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 \u0645\u0631\u0628\u0639 R \u0645\u0639\u062f\u0644 \u0642\u062f\u0631\u0647 0.959<\/strong> .<\/span><\/p>\n
 \u0627\u0644\u062e\u0637\u0648\u0629 3: \u062a\u0635\u0648\u0631 \u0627\u0644\u0645\u0646\u062d\u0646\u0649 \u0627\u0644\u0646\u0647\u0627\u0626\u064a<\/strong><\/span><\/h3>\n
 \u0623\u062e\u064a\u0631\u064b\u0627\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0625\u0646\u0634\u0627\u0621 \u0645\u062e\u0637\u0637 \u0645\u0628\u0639\u062b\u0631 \u0628\u0645\u0646\u062d\u0646\u0649 \u0646\u0645\u0648\u0630\u062c \u0645\u062a\u0639\u062f\u062f \u0627\u0644\u062d\u062f\u0648\u062f \u0645\u0646 \u0627\u0644\u062f\u0631\u062c\u0629 \u0627\u0644\u0631\u0627\u0628\u0639\u0629:<\/span> <\/p>\n
 #create a scatterplot of x vs. y\n<\/span>plot(df$x, df$y, pch=19, xlab=' x<\/span> ', ylab=' y<\/span> ')\n\n#define x-axis values\n<\/span>x_axis <- seq(1, 15, length= 15<\/span> )\n\n#add curve of fourth-degree polynomial model\n<\/span>lines(x_axis, predict(fit4, data. frame<\/span> (x=x_axis)), col=' blue<\/span> ')\n<\/strong><\/pre>\n
\"\u062a\u0631\u0643\u064a\u0628<\/p>\n
 \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u064a\u0636\u064b\u0627 \u0627\u0644\u062d\u0635\u0648\u0644 \u0639\u0644\u0649 \u0645\u0639\u0627\u062f\u0644\u0629 \u0647\u0630\u0627 \u0627\u0644\u062e\u0637 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u0629 Summary()<\/strong> :<\/span><\/span><\/p>\n
 summary(fit4)\n\nCall:\nlm(formula = y ~ poly(x, 4, raw = TRUE), data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-3.4490 -1.1732 0.6023 1.4899 3.0351 \n\nCoefficients:\n                         Estimate Std. Error t value Pr(>|t|)    \n(Intercept) -26.51615 4.94555 -5.362 0.000318 ***\npoly(x, 4, raw = TRUE)1 35.82311 3.98204 8.996 4.15e-06 ***\npoly(x, 4, raw = TRUE)2 -8.36486 0.96791 -8.642 5.95e-06 ***\npoly(x, 4, raw = TRUE)3 0.70812 0.08954 7.908 1.30e-05 ***\npoly(x, 4, raw = TRUE)4 -0.01924 0.00278 -6.922 4.08e-05 ***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 2.424 on 10 degrees of freedom\nMultiple R-squared: 0.9707, Adjusted R-squared: 0.959 \nF-statistic: 82.92 on 4 and 10 DF, p-value: 1.257e-07\n<\/strong><\/pre>\n
 \u0645\u0639\u0627\u062f\u0644\u0629 \u0627\u0644\u0645\u0646\u062d\u0646\u0649 \u0647\u064a \u0643\u0645\u0627 \u064a\u0644\u064a:<\/span><\/p>\n
 \u0635 = -0.0192×4<\/sup> + 0.7081×3<\/sup> – 8.3649×2<\/sup> + 35.823x – 26.516<\/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\u062a\u0646\u0628\u0624 \u0628\u0642\u064a\u0645\u0629 \u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629<\/a> \u0628\u0646\u0627\u0621\u064b \u0639\u0644\u0649 \u0627\u0644\u0645\u062a\u063a\u064a\u0631\u0627\u062a \u0627\u0644\u0645\u062a\u0648\u0642\u0639\u0629 \u0641\u064a \u0627\u0644\u0646\u0645\u0648\u0630\u062c. \u0639\u0644\u0649 \u0633\u0628\u064a\u0644 \u0627\u0644\u0645\u062b\u0627\u0644\u060c \u0625\u0630\u0627 \u0643\u0627\u0646\u062a x<\/em> = 4 \u0641\u0625\u0646\u0646\u0627 \u0646\u062a\u0648\u0642\u0639 \u0623\u0646 y<\/em> = 23.34<\/strong> :<\/span><\/p>\n
 \u0635 = -0.0192(4) 4<\/sup> + 0.7081(4) 3<\/sup> \u2013 8.3649(4) 2<\/sup> + 35.823(4) \u2013 26.516 = 23.34<\/span><\/p>\n
 \u0645\u0635\u0627\u062f\u0631 \u0625\u0636\u0627\u0641\u064a\u0629<\/strong><\/span><\/h3>\n
 \u0645\u0642\u062f\u0645\u0629 \u0644\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0645\u062a\u0639\u062f\u062f \u0627\u0644\u062d\u062f\u0648\u062f
<\/a> \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0645\u062a\u0639\u062f\u062f \u0627\u0644\u062d\u062f\u0648\u062f \u0641\u064a R (\u062e\u0637\u0648\u0629 \u0628\u062e\u0637\u0648\u0629)<\/a>
 \u0643\u064a\u0641\u064a\u0629 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u0629 seq \u0641\u064a R<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0641\u064a \u0643\u062b\u064a\u0631 \u0645\u0646 \u0627\u0644\u0623\u062d\u064a\u0627\u0646 \u0642\u062f \u062a\u0631\u063a\u0628 \u0641\u064a \u0627\u0644\u0639\u062b\u0648\u0631 \u0639\u0644\u0649 \u0627\u0644\u0645\u0639\u0627\u062f\u0644\u0629 \u0627\u0644\u062a\u064a \u062a\u0646\u0627\u0633\u0628 \u0645\u0646\u062d\u0646\u0649 R. \u064a\u0634\u0631\u062d \u0627\u0644\u0645\u062b\u0627\u0644 \u0627\u0644\u062a\u0627\u0644\u064a \u062e\u0637\u0648\u0629 \u0628\u062e\u0637\u0648\u0629 \u0643\u064a\u0641\u064a\u0629 \u0645\u0644\u0627\u0621\u0645\u0629 \u0627\u0644\u0645\u0646\u062d\u0646\u064a\u0627\u062a \u0644\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0641\u064a R \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u0629 poly() \u0648\u0643\u064a\u0641\u064a\u0629 \u062a\u062d\u062f\u064a\u062f \u0627\u0644\u0645\u0646\u062d\u0646\u0649 \u0627\u0644\u0630\u064a \u064a\u0646\u0627\u0633\u0628 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0628\u0634\u0643\u0644 \u0623\u0641\u0636\u0644. \u0627\u0644\u062e\u0637\u0648\u0629 1: \u0625\u0646\u0634\u0627\u0621 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0648\u062a\u0635\u0648\u0631\u0647\u0627 \u0644\u0646\u0628\u062f\u0623 \u0628\u0625\u0646\u0634\u0627\u0621 \u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a \u0645\u0632\u064a\u0641\u0629\u060c \u062b\u0645 \u0646\u0646\u0634\u0626 \u0645\u062e\u0637\u0637\u064b\u0627 \u0645\u0628\u0639\u062b\u0631\u064b\u0627 \u0644\u062a\u0635\u0648\u0631 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a: #create data frame […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"yoast_head":"\n\u062a\u0631\u0643\u064a\u0628 \u0627\u0644\u0645\u0646\u062d\u0646\u0649 \u0641\u064a R (\u0645\u0639 \u0623\u0645\u062b\u0644\u0629) - Statorials<\/title>\n<meta name=\"description\" content=\"\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 \u0645\u0644\u0627\u0621\u0645\u0629 \u0627\u0644\u0645\u0646\u062d\u0646\u0649 \u0644\u0633\u062d\u0627\u0628\u0629 \u0646\u0642\u0637\u064a\u0629 \u0641\u064a \u0644\u063a\u0629 R\u060c \u0645\u0639 \u0639\u062f\u0629 \u0623\u0645\u062b\u0644\u0629.\" \/>\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\/ar\/\u062a\u0639\u062f\u064a\u0644-\u0645\u0646\u062d\u0646\u0649-r\/\" \/>\n<meta property=\"og:locale\" content=\"az_AZ\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u062a\u0631\u0643\u064a\u0628 \u0627\u0644\u0645\u0646\u062d\u0646\u0649 \u0641\u064a R (\u0645\u0639 \u0623\u0645\u062b\u0644\u0629) - 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