{"id":1716,"date":"2023-07-25T06:45:39","date_gmt":"2023-07-25T06:45:39","guid":{"rendered":"https:\/\/statorials.org\/tr\/lanova-sonuclarini-r-cinsinden-yorumlama\/"},"modified":"2023-07-25T06:45:39","modified_gmt":"2023-07-25T06:45:39","slug":"lanova-sonuclarini-r-cinsinden-yorumlama","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/lanova-sonuclarini-r-cinsinden-yorumlama\/","title":{"rendered":"Tam k\u0131lavuz: r'de anova sonu\u00e7lar\u0131 nas\u0131l yorumlan\u0131r?"},"content":{"rendered":"

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\u00dc\u00e7 veya daha fazla ba\u011f\u0131ms\u0131z grubun ortalamalar\u0131 aras\u0131nda istatistiksel olarak anlaml\u0131 bir fark olup olmad\u0131\u011f\u0131n\u0131 belirlemek i\u00e7in tek y\u00f6nl\u00fc ANOVA<\/a> kullan\u0131l\u0131r.<\/span><\/p>\n

Bu e\u011fitim, R’de tek y\u00f6nl\u00fc ANOVA sonu\u00e7lar\u0131n\u0131n nas\u0131l yorumlanaca\u011f\u0131na dair eksiksiz bir k\u0131lavuz sa\u011flar.<\/span><\/p>\n

1. Ad\u0131m: Verileri olu\u015fturun<\/strong><\/h3>\n

\u00dc\u00e7 farkl\u0131 antrenman program\u0131n\u0131n bireylerde farkl\u0131 ortalama kilo kayb\u0131na yol a\u00e7\u0131p a\u00e7mad\u0131\u011f\u0131n\u0131 belirlemek istedi\u011fimizi varsayal\u0131m.<\/span><\/p>\n

Bunu test etmek i\u00e7in,<\/span> 30 ki\u015fiyi bir ay boyunca Program A, Program B veya Program C’yi takip edecek \u015fekilde rastgele atad\u0131\u011f\u0131m\u0131z bir deneye kat\u0131lmak \u00fczere 90 ki\u015fiyi i\u015fe al\u0131yoruz.<\/span><\/p>\n

A\u015fa\u011f\u0131daki kod \u00e7al\u0131\u015faca\u011f\u0131m\u0131z veri \u00e7er\u00e7evesini olu\u015fturur:<\/span><\/p>\n

 #make this example reproducible\nset. seeds<\/span> (0)\n<\/span>\n#create data frame\ndata <- data. frame<\/span> (program = rep(c(' A<\/span> ', ' B<\/span> ', ' C<\/span> '), each = 30),\n                   weight_loss = c(runif(30, 0, 3),\n                                   runif(30, 0, 5),\n                                   runif(30, 1, 7)))<\/span>\n\n#view first six rows of data frame\nhead(data)\n<\/span>\nprogram weight_loss\n1 A 2.6900916\n2 A 0.7965260\n3 A 1.1163717\n4 A 1.7185601\n5 A 2.7246234\n6 A 0.6050458<\/span>\n<\/span><\/strong><\/pre>\n
 Ad\u0131m 2: ANOVA’y\u0131 ger\u00e7ekle\u015ftirin<\/strong><\/h3>\n
 Daha sonra, tek y\u00f6nl\u00fc bir ANOVA ger\u00e7ekle\u015ftirmek i\u00e7in aov()<\/strong> komutunu kullanaca\u011f\u0131z:<\/span><\/p>\n
 #fit one-way ANOVA model\nmodel <- aov(weight_loss ~ program, data = data)<\/span>\n<\/span><\/strong><\/pre>\n
 3. Ad\u0131m: ANOVA sonu\u00e7lar\u0131n\u0131 yorumlay\u0131n<\/strong><\/span><\/h3>\n
 Daha sonra, tek y\u00f6nl\u00fc ANOVA sonu\u00e7lar\u0131n\u0131 g\u00f6r\u00fcnt\u00fclemek i\u00e7in Summary()<\/strong> komutunu kullanaca\u011f\u0131z:<\/span><\/p>\n
 #view summary of one-way ANOVA model\nsummary(model)\n\n            Df Sum Sq Mean Sq F value Pr(>F)    \nprogram 2 98.93 49.46 30.83 7.55e-11 ***\nResiduals 87 139.57 1.60                     \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n<\/span><\/span><\/strong><\/pre>\n
 Her sonu\u00e7 de\u011ferinin nas\u0131l yorumlanaca\u011f\u0131 a\u015fa\u011f\u0131da a\u00e7\u0131klanm\u0131\u015ft\u0131r:<\/span><\/p>\n
 Program Df:<\/strong> De\u011fi\u015fken program\u0131n<\/em> serbestlik derecesi. Bu #groups -1 olarak hesaplan\u0131r. Bu durumda 3 farkl\u0131 antrenman program\u0131 vard\u0131, dolay\u0131s\u0131yla bu de\u011fer: 3-1 = 2<\/strong> .<\/span><\/p>\n
 Df Art\u0131klar:<\/strong> Art\u0131klar\u0131n serbestlik derecesi. Bu, #toplam g\u00f6zlemler \u2013 #gruplar olarak hesaplan\u0131r. Bu durumda 90 g\u00f6zlem ve 3 grup vard\u0131, dolay\u0131s\u0131yla bu de\u011fer: 90 -3 = 87<\/strong> .<\/span><\/p>\n
 Program Toplam\u0131 Kare:<\/strong> De\u011fi\u015fken programla<\/em> ili\u015fkili karelerin toplam\u0131. Bu de\u011fer 98,93’t\u00fcr<\/strong> .<\/span><\/p>\n
 Art\u0131klar\u0131n karelerinin toplam\u0131:<\/strong> art\u0131klarla veya \u201chatalarla\u201d ili\u015fkili karelerin toplam\u0131. Bu de\u011fer 139,57’dir<\/strong> .<\/span><\/p>\n
 Orta kare. Program:<\/strong> Programla ili\u015fkili karelerin ortalama toplam\u0131. Bu, kare toplam\u0131 olarak hesaplan\u0131r. program \/ program Df. Bu durumda \u015fu \u015fekilde hesaplan\u0131r: 98,93 \/ 2 = 49,46<\/strong> .<\/span><\/p>\n
 Orta kare. Art\u0131klar:<\/strong> art\u0131klarla ili\u015fkili karelerin ortalama toplam\u0131. Bu, kare toplam\u0131 olarak hesaplan\u0131r. kal\u0131nt\u0131lar \/ kal\u0131nt\u0131lar Df. Bu durumda \u015fu \u015fekilde hesaplan\u0131r: 139,57 \/ 87 = 1,60<\/strong> .<\/span><\/p>\n
 F de\u011feri:<\/strong> ANOVA modelinin genel F istatisti\u011fi. Bu ortalama kare olarak hesaplan\u0131r. program \/ Ortalama kare. Kal\u0131nt\u0131lar. Bu durumda \u015fu \u015fekilde hesaplan\u0131r: 49,46 \/ 1,60 = 30,83<\/strong> .<\/span><\/p>\n
 Pr(>F):<\/strong> Pay\u0131 df = 2 ve paydas\u0131 df = 87 olan F istatisti\u011fiyle ili\u015fkili p de\u011feri. Bu durumda p de\u011feri 7,552e-11’dir<\/strong> ve bu son derece k\u00fc\u00e7\u00fck bir say\u0131d\u0131r.<\/span><\/p>\n
 Sonu\u00e7 k\u00fcmesindeki en \u00f6nemli de\u011fer p de\u011feridir \u00e7\u00fcnk\u00fc bize \u00fc\u00e7 grup aras\u0131nda ortalama de\u011ferler aras\u0131nda anlaml\u0131 bir fark olup olmad\u0131\u011f\u0131n\u0131 s\u00f6yler.<\/span><\/p>\n
 Tek y\u00f6nl\u00fc bir ANOVA’n\u0131n a\u015fa\u011f\u0131daki bo\u015f ve alternatif hipotezleri kulland\u0131\u011f\u0131n\u0131 hat\u0131rlay\u0131n:<\/span><\/p>\n