{"id":1239,"date":"2023-07-27T04:19:56","date_gmt":"2023-07-27T04:19:56","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d8%aa%d8%b5%d8%ad%d9%8a%d8%ad-%d8%a8%d9%88%d9%86%d9%81%d9%8a%d8%b1%d9%88%d9%86%d9%8a-%d9%81%d9%8a-%d8%b5\/"},"modified":"2023-07-27T04:19:56","modified_gmt":"2023-07-27T04:19:56","slug":"%d8%aa%d8%b5%d8%ad%d9%8a%d8%ad-%d8%a8%d9%88%d9%86%d9%81%d9%8a%d8%b1%d9%88%d9%86%d9%8a-%d9%81%d9%8a-%d8%b5","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/%d8%aa%d8%b5%d8%ad%d9%8a%d8%ad-%d8%a8%d9%88%d9%86%d9%81%d9%8a%d8%b1%d9%88%d9%86%d9%8a-%d9%81%d9%8a-%d8%b5\/","title":{"rendered":"\u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u062a\u0635\u062d\u064a\u062d bonferroni \u0641\u064a r"},"content":{"rendered":"

<\/p>\n

\n

\u064a\u062a\u0645 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 ANOVA \u0623\u062d\u0627\u062f\u064a \u0627\u0644\u0627\u062a\u062c\u0627\u0647<\/a> \u0644\u062a\u062d\u062f\u064a\u062f \u0645\u0627 \u0625\u0630\u0627 \u0643\u0627\u0646 \u0647\u0646\u0627\u0643 \u0641\u0631\u0642 \u0630\u0648 \u062f\u0644\u0627\u0644\u0629 \u0625\u062d\u0635\u0627\u0626\u064a\u0629 \u0628\u064a\u0646 \u0645\u062a\u0648\u0633\u0637\u0627\u062a \u062b\u0644\u0627\u062b \u0645\u062c\u0645\u0648\u0639\u0627\u062a \u0645\u0633\u062a\u0642\u0644\u0629 \u0623\u0648 \u0623\u0643\u062b\u0631 \u0623\u0645 \u0644\u0627.<\/span><\/p>\n

\u0625\u0630\u0627 \u0643\u0627\u0646\u062a \u0627\u0644\u0642\u064a\u0645\u0629 \u0627\u0644\u0627\u062d\u062a\u0645\u0627\u0644\u064a\u0629 \u0627\u0644\u0625\u062c\u0645\u0627\u0644\u064a\u0629 \u0644\u062c\u062f\u0648\u0644 \u062a\u062d\u0644\u064a\u0644 \u0627\u0644\u062a\u0628\u0627\u064a\u0646 (ANOVA) \u0623\u0642\u0644 \u0645\u0646 \u0645\u0633\u062a\u0648\u0649 \u0645\u0639\u064a\u0646 \u0645\u0646 \u0627\u0644\u0623\u0647\u0645\u064a\u0629\u060c \u0641\u0644\u062f\u064a\u0646\u0627 \u0623\u062f\u0644\u0629 \u0643\u0627\u0641\u064a\u0629 \u0644\u0646\u0642\u0648\u0644 \u0623\u0646 \u0625\u062d\u062f\u0649 \u0648\u0633\u0627\u0626\u0644 \u0627\u0644\u0645\u062c\u0645\u0648\u0639\u0629 \u0639\u0644\u0649 \u0627\u0644\u0623\u0642\u0644 \u062a\u062e\u062a\u0644\u0641 \u0639\u0646 \u0627\u0644\u0648\u0633\u0627\u0626\u0644 \u0627\u0644\u0623\u062e\u0631\u0649.<\/span><\/p>\n

\u0648\u0645\u0639 \u0630\u0644\u0643\u060c \u0647\u0630\u0627 \u0644\u0627 \u064a\u062e\u0628\u0631\u0646\u0627 \u0645\u0627 \u0647\u064a<\/em> \u0627\u0644\u0645\u062c\u0645\u0648\u0639\u0627\u062a \u0627\u0644\u062a\u064a \u062a\u062e\u062a\u0644\u0641 \u0639\u0646 \u0628\u0639\u0636\u0647\u0627 \u0627\u0644\u0628\u0639\u0636. \u0648\u0647\u0630\u0627 \u064a\u062e\u0628\u0631\u0646\u0627 \u0628\u0628\u0633\u0627\u0637\u0629 \u0623\u0646\u0647 \u0644\u064a\u0633\u062a \u0643\u0644 \u0645\u062a\u0648\u0633\u0637\u0627\u062a \u0627\u0644\u0645\u062c\u0645\u0648\u0639\u0629 \u0645\u062a\u0633\u0627\u0648\u064a\u0629.<\/span><\/p>\n

\u0645\u0646 \u0623\u062c\u0644 \u0645\u0639\u0631\u0641\u0629 \u0627\u0644\u0645\u062c\u0645\u0648\u0639\u0627\u062a \u0627\u0644\u062a\u064a \u062a\u062e\u062a\u0644\u0641 \u0639\u0646 \u0628\u0639\u0636\u0647\u0627 \u0627\u0644\u0628\u0639\u0636 \u0628\u0627\u0644\u0636\u0628\u0637\u060c \u0646\u062d\u062a\u0627\u062c \u0625\u0644\u0649 \u0625\u062c\u0631\u0627\u0621 \u0627\u062e\u062a\u0628\u0627\u0631\u0627\u062a t \u0632\u0648\u062c\u064a\u0629 \u0628\u064a\u0646 \u0643\u0644 \u0645\u062c\u0645\u0648\u0639\u0629<\/span> \u0645\u0639 \u0627\u0644\u062a\u062d\u0643\u0645 \u0641\u064a \u0645\u0639\u062f\u0644 \u0627\u0644\u062e\u0637\u0623 \u0627\u0644\u0639\u0627\u0626\u0644\u064a<\/a> .<\/span><\/p>\n

\u0625\u062d\u062f\u0649 \u0627\u0644\u0637\u0631\u0642 \u0627\u0644\u0623\u0643\u062b\u0631 \u0634\u064a\u0648\u0639\u064b\u0627 \u0644\u062a\u062d\u0642\u064a\u0642 \u0630\u0644\u0643 \u0647\u064a \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u062a\u0635\u062d\u064a\u062d Bonferroni<\/strong> \u0639\u0646\u062f \u062d\u0633\u0627\u0628 \u0627\u0644\u0642\u064a\u0645 p \u0644\u0643\u0644 \u0627\u062e\u062a\u0628\u0627\u0631 \u0645\u0646 \u0627\u062e\u062a\u0628\u0627\u0631\u0627\u062a t \u0627\u0644\u0632\u0648\u062c\u064a\u0629.<\/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\u062c\u0631\u0627\u0621 \u062a\u0635\u062d\u064a\u062d Bonferroni \u0641\u064a R.<\/span><\/p>\n

\u0645\u062b\u0627\u0644: \u062a\u0635\u062d\u064a\u062d Bonferroni \u0641\u064a R<\/strong><\/span><\/h3>\n

\u0644\u0646\u0641\u062a\u0631\u0636 \u0623\u0646 \u0627\u0644\u0645\u0639\u0644\u0645 \u064a\u0631\u064a\u062f \u0645\u0639\u0631\u0641\u0629 \u0645\u0627 \u0625\u0630\u0627 \u0643\u0627\u0646\u062a \u062b\u0644\u0627\u062b \u062a\u0642\u0646\u064a\u0627\u062a \u062f\u0631\u0627\u0633\u0629 \u0645\u062e\u062a\u0644\u0641\u0629 \u062a\u0624\u062f\u064a \u0625\u0644\u0649 \u062f\u0631\u062c\u0627\u062a \u0627\u062e\u062a\u0628\u0627\u0631 \u0645\u062e\u062a\u0644\u0641\u0629 \u0628\u064a\u0646 \u0627\u0644\u0637\u0644\u0627\u0628 \u0623\u0645 \u0644\u0627.<\/span><\/p>\n

\u0648\u0644\u0627\u062e\u062a\u0628\u0627\u0631 \u0630\u0644\u0643\u060c \u0642\u0627\u0645\u062a \u0628\u062a\u0639\u064a\u064a\u0646 10 \u0637\u0644\u0627\u0628 \u0628\u0634\u0643\u0644 \u0639\u0634\u0648\u0627\u0626\u064a \u0644\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0643\u0644 \u0623\u0633\u0644\u0648\u0628 \u0645\u0646 \u0623\u0633\u0627\u0644\u064a\u0628 \u0627\u0644\u062f\u0631\u0627\u0633\u0629.<\/span> \u0648\u0628\u0639\u062f \u0623\u0633\u0628\u0648\u0639 \u0645\u0646 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0623\u0633\u0644\u0648\u0628 \u0627\u0644\u062f\u0631\u0627\u0633\u0629 \u0627\u0644\u0645\u062e\u0635\u0635 \u0644\u0647\u0645\u060c \u064a\u0642\u0648\u0645 \u0643\u0644 \u0637\u0627\u0644\u0628 \u0628\u0625\u062c\u0631\u0627\u0621 \u0646\u0641\u0633 \u0627\u0644\u0627\u062e\u062a\u0628\u0627\u0631.<\/span><\/p>\n

\u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062e\u0637\u0648\u0627\u062a \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0641\u064a R \u0644\u062a\u0646\u0627\u0633\u0628 ANOVA \u0623\u062d\u0627\u062f\u064a \u0627\u0644\u0627\u062a\u062c\u0627\u0647 \u0648\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u062a\u0635\u062d\u064a\u062d Bonferroni \u0644\u062d\u0633\u0627\u0628 \u0627\u0644\u0627\u062e\u062a\u0644\u0627\u0641\u0627\u062a \u0627\u0644\u0632\u0648\u062c\u064a\u0629 \u0628\u064a\u0646 \u0646\u062a\u0627\u0626\u062c \u0627\u062e\u062a\u0628\u0627\u0631 \u0643\u0644 \u0645\u062c\u0645\u0648\u0639\u0629.<\/span><\/p>\n

\u0627\u0644\u062e\u0637\u0648\u0629 1: \u0625\u0646\u0634\u0627\u0621 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a.<\/span><\/strong><\/p>\n

\u064a\u0648\u0636\u062d \u0627\u0644\u0643\u0648\u062f \u0627\u0644\u062a\u0627\u0644\u064a \u0643\u064a\u0641\u064a\u0629 \u0625\u0646\u0634\u0627\u0621 \u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a \u062a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 \u0646\u062a\u0627\u0626\u062c \u0627\u0644\u0627\u0645\u062a\u062d\u0627\u0646\u0627\u062a \u0644\u062c\u0645\u064a\u0639 \u0627\u0644\u0637\u0644\u0627\u0628 \u0627\u0644\u062b\u0644\u0627\u062b\u064a\u0646:<\/span><\/p>\n

 #create data frame<\/span>\ndata <- data.frame(technique = rep<\/span> (c(\"tech1\", \"tech2\", \"tech3\"), each<\/span> = 10<\/span> ),\n                   score = c(76, 77, 77, 81, 82, 82, 83, 84, 85, 89,\n                             81, 82, 83, 83, 83, 84, 87, 90, 92, 93,\n                             77, 78, 79, 88, 89, 90, 91, 95, 95, 98))\n\n#view first six rows of data frame<\/span>\nhead(data)\n\n  technical score\n1 tech1 76\n2 tech1 77\n3 tech1 77\n4 tech1 81\n5 tech1 82\n6 tech1 82\n<\/strong><\/pre>\n
 \u0627\u0644\u062e\u0637\u0648\u0629 \u0627\u0644\u062b\u0627\u0646\u064a\u0629: \u0639\u0631\u0636 \u0646\u062a\u0627\u0626\u062c \u0627\u0644\u0627\u0645\u062a\u062d\u0627\u0646\u0627\u062a \u0644\u0643\u0644 \u0645\u062c\u0645\u0648\u0639\u0629.<\/span><\/strong><\/p>\n
 \u064a\u0648\u0636\u062d \u0627\u0644\u0643\u0648\u062f \u0627\u0644\u062a\u0627\u0644\u064a \u0643\u064a\u0641\u064a\u0629 \u0625\u0646\u062a\u0627\u062c boxplots \u0644\u062a\u0635\u0648\u0631 \u062a\u0648\u0632\u064a\u0639 \u0646\u062a\u0627\u0626\u062c \u0627\u0644\u0627\u062e\u062a\u0628\u0627\u0631 \u0644\u0643\u0644 \u0645\u062c\u0645\u0648\u0639\u0629:<\/span> <\/p>\n
 boxplot(score ~ technique,\n        data = data,\n        main = \"Exam Scores by Studying Technique\",\n        xlab = \"Studying Technique\",\n        ylab = \"Exam Scores\",\n        col = \"steelblue\",\n        border = \"black\")\n<\/strong><\/pre>\n
\"\u062a\u0635\u062d\u064a\u062d<\/p>\n
 \u0627\u0644\u062e\u0637\u0648\u0629 3: \u0625\u062c\u0631\u0627\u0621 \u062a\u062d\u0644\u064a\u0644 \u0627\u0644\u062a\u0628\u0627\u064a\u0646 (ANOVA) \u0641\u064a \u0627\u062a\u062c\u0627\u0647 \u0648\u0627\u062d\u062f.<\/span><\/strong><\/p>\n
 \u064a\u0648\u0636\u062d \u0627\u0644\u0643\u0648\u062f \u0627\u0644\u062a\u0627\u0644\u064a \u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u062a\u062d\u0644\u064a\u0644 \u0627\u0644\u062a\u0628\u0627\u064a\u0646 (ANOVA) \u0623\u062d\u0627\u062f\u064a \u0627\u0644\u0627\u062a\u062c\u0627\u0647 \u0644\u0627\u062e\u062a\u0628\u0627\u0631 \u0627\u0644\u0627\u062e\u062a\u0644\u0627\u0641\u0627\u062a \u0628\u064a\u0646 \u0645\u062a\u0648\u0633\u0637 \u062f\u0631\u062c\u0627\u062a \u0627\u0644\u0627\u062e\u062a\u0628\u0627\u0631 \u0641\u064a \u0643\u0644 \u0645\u062c\u0645\u0648\u0639\u0629:<\/span><\/p>\n
 #fit the one-way ANOVA model<\/span>\nmodel <- aov(score ~ technique, data = data)\n\n#view model output\n<\/span>summary(model)\n\n            Df Sum Sq Mean Sq F value Pr(>F)  \ntechnical 2 211.5 105.73 3.415 0.0476 *\nResiduals 27 836.0 30.96                 \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<\/strong><\/pre>\n
 \u0648\u0628\u0645\u0627 \u0623\u0646 \u0627\u0644\u0642\u064a\u0645\u0629 \u0627\u0644\u0627\u062d\u062a\u0645\u0627\u0644\u064a\u0629 \u0627\u0644\u0625\u062c\u0645\u0627\u0644\u064a\u0629 ( 0.0476<\/strong> ) \u0623\u0642\u0644 \u0645\u0646 0.05\u060c \u0641\u0647\u0630\u0627 \u064a\u0634\u064a\u0631 \u0625\u0644\u0649 \u0623\u0646 \u0643\u0644 \u0645\u062c\u0645\u0648\u0639\u0629 \u0644\u064a\u0633 \u0644\u062f\u064a\u0647\u0627 \u0646\u0641\u0633 \u0645\u062a\u0648\u0633\u0637 \u062f\u0631\u062c\u0627\u062a \u0627\u0644\u0627\u062e\u062a\u0628\u0627\u0631.<\/span><\/p>\n
 \u0628\u0639\u062f \u0630\u0644\u0643\u060c \u0633\u0646\u0642\u0648\u0645 \u0628\u0625\u062c\u0631\u0627\u0621 \u0627\u062e\u062a\u0628\u0627\u0631\u0627\u062a t \u0627\u0644\u0632\u0648\u062c\u064a\u0629 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u062a\u0635\u062d\u064a\u062d Bonferroni \u0644\u0644\u0642\u064a\u0645 p \u0644\u062d\u0633\u0627\u0628 \u0627\u0644\u0641\u0631\u0648\u0642 \u0627\u0644\u0632\u0648\u062c\u064a\u0629 \u0628\u064a\u0646 \u0646\u062a\u0627\u0626\u062c \u0627\u062e\u062a\u0628\u0627\u0631 \u0643\u0644 \u0645\u062c\u0645\u0648\u0639\u0629.<\/span><\/p>\n
 \u0627\u0644\u062e\u0637\u0648\u0629 4: \u0625\u062c\u0631\u0627\u0621 \u0627\u062e\u062a\u0628\u0627\u0631\u0627\u062a t \u0627\u0644\u0632\u0648\u062c\u064a\u0629.<\/strong><\/span><\/p>\n
 \u0644\u0625\u062c\u0631\u0627\u0621 \u0627\u062e\u062a\u0628\u0627\u0631\u0627\u062a t \u0627\u0644\u0632\u0648\u062c\u064a\u0629 \u0645\u0639 \u062a\u0635\u062d\u064a\u062d Bonferroni \u0641\u064a R\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u0629 Pairwise.t.test()<\/a> \u060c \u0648\u0627\u0644\u062a\u064a \u062a\u0633\u062a\u062e\u062f\u0645 \u0628\u0646\u0627\u0621 \u0627\u0644\u062c\u0645\u0644\u0629 \u0627\u0644\u062a\u0627\u0644\u064a:<\/span><\/p>\n
 Pairwise.t.test(x, g, p.adjust.method=\u201dbonferroni\u201d)<\/strong><\/span><\/p>\n
 \u0630\u0647\u0628:<\/span><\/p>\n