{"id":3218,"date":"2023-07-18T15:10:25","date_gmt":"2023-07-18T15:10:25","guid":{"rendered":"https:\/\/statorials.org\/ar\/r-%d9%82%d8%a7%d9%8a%d9%94%d9%85%d8%a9-%d9%86%d9%88%d8%b9-%d8%ba%d9%8a%d8%b1-%d8%b5%d8%a7%d9%84%d8%ad%d8%a9-%d9%84%d9%84%d9%85%d8%aa%d8%ba%d9%8a%d8%b1\/"},"modified":"2023-07-18T15:10:25","modified_gmt":"2023-07-18T15:10:25","slug":"r-%d9%82%d8%a7%d9%8a%d9%94%d9%85%d8%a9-%d9%86%d9%88%d8%b9-%d8%ba%d9%8a%d8%b1-%d8%b5%d8%a7%d9%84%d8%ad%d8%a9-%d9%84%d9%84%d9%85%d8%aa%d8%ba%d9%8a%d8%b1","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/r-%d9%82%d8%a7%d9%8a%d9%94%d9%85%d8%a9-%d9%86%d9%88%d8%b9-%d8%ba%d9%8a%d8%b1-%d8%b5%d8%a7%d9%84%d8%ad%d8%a9-%d9%84%d9%84%d9%85%d8%aa%d8%ba%d9%8a%d8%b1\/","title":{"rendered":"\u0643\u064a\u0641\u064a\u0629 \u0627\u0644\u0625\u0635\u0644\u0627\u062d \u0641\u064a r: \u0646\u0648\u0639 (\u0642\u0627\u0626\u0645\u0629) \u063a\u064a\u0631 \u0635\u0627\u0644\u062d \u0644\u0644\u0645\u062a\u063a\u064a\u0631"},"content":{"rendered":"

<\/p>\n

\n

\u0627\u0644\u062e\u0637\u0623 \u0627\u0644\u0630\u064a \u0642\u062f \u062a\u0648\u0627\u062c\u0647\u0647 \u0641\u064a R \u0647\u0648:<\/span><\/p>\n

 Error in model.frame.default(formula = y ~ x, drop.unused.levels = TRUE): \n  invalid type (list) for variable 'x' \n<\/strong><\/pre>\n
 \u064a\u062d\u062f\u062b \u0647\u0630\u0627 \u0627\u0644\u062e\u0637\u0623 \u0639\u0627\u062f\u0629\u064b \u0639\u0646\u062f \u0645\u062d\u0627\u0648\u0644\u0629 \u0645\u0644\u0627\u0621\u0645\u0629 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0623\u0648 \u0646\u0645\u0648\u0630\u062c ANOVA \u0641\u064a R \u0648\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0642\u0627\u0626\u0645\u0629<\/strong> \u0644\u0623\u062d\u062f \u0627\u0644\u0645\u062a\u063a\u064a\u0631\u0627\u062a \u0628\u062f\u0644\u0627\u064b \u0645\u0646 \u0627\u0644\u0645\u062a\u062c\u0647<\/strong> .<\/span><\/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\u0635\u0644\u0627\u062d \u0647\u0630\u0627 \u0627\u0644\u062e\u0637\u0623 \u0639\u0645\u0644\u064a\u064b\u0627.<\/span><\/p>\n
 \u0643\u064a\u0641\u064a\u0629 \u0625\u0639\u0627\u062f\u0629 \u0625\u0646\u062a\u0627\u062c \u0627\u0644\u062e\u0637\u0623<\/strong><\/h2>\n
 \u0644\u0646\u0641\u062a\u0631\u0636 \u0623\u0646\u0646\u064a \u0623\u062d\u0627\u0648\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\u0628\u0633\u064a\u0637<\/a> \u0641\u064a R:<\/span><\/p>\n
 #define variables\nx <- list(1, 4, 4, 5, 7, 8, 9, 10, 13, 14)\ny <- c(10, 13, 13, 14, 18, 20, 22, 24, 29, 31)<\/span>\n\n#attempt to fit regression model\nmodel <- lm(y ~ x)\n\nError in model.frame.default(formula = y ~ x, drop.unused.levels = TRUE): \n  invalid type (list) for variable 'x'\n<\/span><\/span><\/strong><\/span><\/pre>\n
 \u062a\u0638\u0647\u0631 \u0644\u064a \u0631\u0633\u0627\u0644\u0629 \u062e\u0637\u0623 \u0644\u0623\u0646 \u0627\u0644\u062f\u0627\u0644\u0629 lm()<\/strong> \u064a\u0645\u0643\u0646\u0647\u0627 \u0641\u0642\u0637 \u0623\u062e\u0630 \u0627\u0644\u0645\u062a\u062c\u0647\u0627\u062a \u0643\u0645\u062f\u062e\u0644\u0627\u062a \u0648\u0627\u0644\u0645\u062a\u063a\u064a\u0631 x \u0639\u0628\u0627\u0631\u0629 \u0639\u0646 \u0642\u0627\u0626\u0645\u0629 \u062d\u0627\u0644\u064a\u064b\u0627.<\/span><\/p>\n
 \u0643\u064a\u0641\u064a\u0629 \u062a\u062c\u0646\u0628 \u0627\u0644\u062e\u0637\u0623<\/strong><\/span><\/h2>\n
 \u0623\u0633\u0647\u0644 \u0637\u0631\u064a\u0642\u0629 \u0644\u062a\u062c\u0646\u0628 \u0647\u0630\u0627 \u0627\u0644\u062e\u0637\u0623 \u0647\u064a \u0628\u0628\u0633\u0627\u0637\u0629 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u0629 unlist()<\/a> \u0644\u062a\u062d\u0648\u064a\u0644 \u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0642\u0627\u0626\u0645\u0629 \u0625\u0644\u0649 \u0645\u062a\u062c\u0647:<\/span><\/span><\/p>\n
 #define variables\nx <- list(1, 4, 4, 5, 7, 8, 9, 10, 13, 14)\ny <- c(10, 13, 13, 14, 18, 20, 22, 24, 29, 31)\n\n#attempt to fit regression model\n<\/span>model <- lm(y ~ unlist(x))\n\n#view the model output<\/span>\nsummary(model)\n\nCall:\nlm(formula = y ~ unlist(x))\n\nResiduals:\n    Min 1Q Median 3Q Max \n-1.1282 -0.4194 -0.1087 0.2966 1.7068 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)    \n(Intercept) 6.58447 0.55413 11.88 2.31e-06 ***\nunlist(x) 1.70874 0.06544 26.11 4.97e-09 ***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 0.8134 on 8 degrees of freedom\nMultiple R-squared: 0.9884, Adjusted R-squared: 0.987 \nF-statistic: 681.8 on 1 and 8 DF, p-value: 4.97e-09\n<\/span><\/span><\/strong><\/span><\/pre>\n
 \u0644\u0627\u062d\u0638 \u0623\u0646\u0646\u0627 \u0642\u0627\u062f\u0631\u0648\u0646 \u0639\u0644\u0649 \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\u0628\u0633\u064a\u0637 \u062f\u0648\u0646 \u0623\u064a \u0623\u062e\u0637\u0627\u0621 \u0647\u0630\u0647 \u0627\u0644\u0645\u0631\u0629 \u0644\u0623\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0645\u0646\u0627 unlist()<\/strong> \u0644\u062a\u062d\u0648\u064a\u0644 \u0627\u0644\u0645\u062a\u063a\u064a\u0631 x \u0625\u0644\u0649 \u0645\u062a\u062c\u0647.<\/span><\/p>\n
 #define variables\nx1 <- list(1, 4, 4, 5, 7, 8, 9, 10, 13, 14)\nx2 <- list(20, 16, 16, 15, 16, 12, 10, 8, 8, 4)\ny <- c(10, 13, 13, 14, 18, 20, 22, 24, 29, 31)\n\n#fit multiple linear regression model\n<\/span>model <- lm(y ~ unlist(x1) + unlist(x2))\n\n#view the model output\n<\/span>summary(model)\n\nCall:\nlm(formula = y ~ unlist(x1) + unlist(x2))\n\nResiduals:\n    Min 1Q Median 3Q Max \n-1.1579 -0.4211 -0.1386 0.3108 1.7130 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)    \n(Intercept) 8.34282 4.44971 1.875 0.102932    \nunlist(x1) 1.61339 0.24899 6.480 0.000341 ***\nunlist(x2) -0.08346 0.20937 -0.399 0.702044    \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 0.8599 on 7 degrees of freedom\nMultiple R-squared: 0.9887, Adjusted R-squared: 0.9854 \nF-statistic: 305.1 on 2 and 7 DF, p-value: 1.553e-07<\/span><\/span><\/strong><\/span><\/pre>\n
 \u0645\u0631\u0629 \u0623\u062e\u0631\u0649\u060c \u0646\u062d\u0646 \u0644\u0627 \u0646\u062a\u0644\u0642\u0649 \u0623\u064a \u0623\u062e\u0637\u0627\u0621 \u0644\u0623\u0646\u0646\u0627 \u0642\u0645\u0646\u0627 \u0628\u062a\u062d\u0648\u064a\u0644 \u0643\u0644 \u0643\u0627\u0626\u0646 \u0641\u064a \u0627\u0644\u0642\u0627\u0626\u0645\u0629 \u0625\u0644\u0649 \u0645\u062a\u062c\u0647\u0627\u062a.<\/span><\/span><\/p>\n
 \u0645\u0635\u0627\u062f\u0631 \u0625\u0636\u0627\u0641\u064a\u0629<\/strong><\/span><\/h3>\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\u0639\u0645\u0644\u064a\u0627\u062a \u0627\u0644\u0634\u0627\u0626\u0639\u0629 \u0627\u0644\u0623\u062e\u0631\u0649 \u0641\u064a R:<\/span><\/p>\n
 \u0643\u064a\u0641\u064a\u0629 \u062a\u0641\u0633\u064a\u0631 \u0625\u062e\u0631\u0627\u062c glm \u0641\u064a R<\/a>
 \u0643\u064a\u0641\u064a\u0629 \u062a\u0641\u0633\u064a\u0631 \u0646\u062a\u0627\u0626\u062c ANOVA \u0641\u064a R<\/a>
 \u0643\u064a\u0641\u064a\u0629 \u0627\u0644\u062a\u0639\u0627\u0645\u0644 \u0645\u0639 \u062a\u062d\u0630\u064a\u0631 R: glm.fit: \u0627\u0644\u062e\u0648\u0627\u0631\u0632\u0645\u064a\u0629 \u0644\u0645 \u062a\u062a\u0642\u0627\u0631\u0628<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0627\u0644\u062e\u0637\u0623 \u0627\u0644\u0630\u064a \u0642\u062f \u062a\u0648\u0627\u062c\u0647\u0647 \u0641\u064a R \u0647\u0648: Error in model.frame.default(formula = y ~ x, drop.unused.levels = TRUE): invalid type (list) for variable ‘x’ \u064a\u062d\u062f\u062b \u0647\u0630\u0627 \u0627\u0644\u062e\u0637\u0623 \u0639\u0627\u062f\u0629\u064b \u0639\u0646\u062f \u0645\u062d\u0627\u0648\u0644\u0629 \u0645\u0644\u0627\u0621\u0645\u0629 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0623\u0648 \u0646\u0645\u0648\u0630\u062c ANOVA \u0641\u064a R \u0648\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0642\u0627\u0626\u0645\u0629 \u0644\u0623\u062d\u062f \u0627\u0644\u0645\u062a\u063a\u064a\u0631\u0627\u062a \u0628\u062f\u0644\u0627\u064b \u0645\u0646 \u0627\u0644\u0645\u062a\u062c\u0647 . \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\u0635\u0644\u0627\u062d \u0647\u0630\u0627 \u0627\u0644\u062e\u0637\u0623 \u0639\u0645\u0644\u064a\u064b\u0627. \u0643\u064a\u0641\u064a\u0629 […]<\/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\u0643\u064a\u0641\u064a\u0629 \u0627\u0644\u0625\u0635\u0644\u0627\u062d \u0641\u064a R: \u0646\u0648\u0639 (\u0642\u0627\u0626\u0645\u0629) \u063a\u064a\u0631 \u0635\u0627\u0644\u062d \u0644\u0644\u0645\u062a\u063a\u064a\u0631 - 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 \u0625\u0635\u0644\u0627\u062d \u0627\u0644\u062e\u0637\u0623 \u0627\u0644\u062a\u0627\u0644\u064a \u0641\u064a R: \u0646\u0648\u0639 (\u0642\u0627\u0626\u0645\u0629) \u063a\u064a\u0631 \u0635\u0627\u0644\u062d \u0644\u0644\u0645\u062a\u063a\u064a\u0631.\" \/>\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\/r-\u0642\u0627\u064a\u0654\u0645\u0629-\u0646\u0648\u0639-\u063a\u064a\u0631-\u0635\u0627\u0644\u062d\u0629-\u0644\u0644\u0645\u062a\u063a\u064a\u0631\/\" \/>\n<meta property=\"og:locale\" content=\"az_AZ\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u0643\u064a\u0641\u064a\u0629 \u0627\u0644\u0625\u0635\u0644\u0627\u062d \u0641\u064a R: \u0646\u0648\u0639 (\u0642\u0627\u0626\u0645\u0629) \u063a\u064a\u0631 \u0635\u0627\u0644\u062d \u0644\u0644\u0645\u062a\u063a\u064a\u0631 - Statorials\" \/>\n<meta property=\"og: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 \u0625\u0635\u0644\u0627\u062d \u0627\u0644\u062e\u0637\u0623 \u0627\u0644\u062a\u0627\u0644\u064a \u0641\u064a R: \u0646\u0648\u0639 (\u0642\u0627\u0626\u0645\u0629) \u063a\u064a\u0631 \u0635\u0627\u0644\u062d \u0644\u0644\u0645\u062a\u063a\u064a\u0631.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/ar\/r-\u0642\u0627\u064a\u0654\u0645\u0629-\u0646\u0648\u0639-\u063a\u064a\u0631-\u0635\u0627\u0644\u062d\u0629-\u0644\u0644\u0645\u062a\u063a\u064a\u0631\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-18T15:10:25+00:00\" \/>\n<meta name=\"author\" content=\"\u062f\u0643\u062a\u0648\u0631 \u0628\u0646\u064a\u0627\u0645\u064a\u0646 \u0623\u0646\u062f\u0631\u0633\u0648\u0646\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta 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