{"id":493,"date":"2023-07-29T17:42:56","date_gmt":"2023-07-29T17:42:56","guid":{"rendered":"https:\/\/statorials.org\/tr\/tek-yonlu-anova-r\/"},"modified":"2023-07-29T17:42:56","modified_gmt":"2023-07-29T17:42:56","slug":"tek-yonlu-anova-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/tek-yonlu-anova-r\/","title":{"rendered":"R&#39;de tek y\u00f6nl\u00fc anova nas\u0131l ger\u00e7ekle\u015ftirilir"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\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 <a href=\"https:\/\/statorials.org\/tr\/tek-yonlu-anova\/\" target=\"_blank\" rel=\"noopener\">tek y\u00f6nl\u00fc ANOVA<\/a> kullan\u0131l\u0131r.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Bu t\u00fcr testlere <em>tek y\u00f6nl\u00fc<\/em> ANOVA denir \u00e7\u00fcnk\u00fc <em>bir<\/em> yorday\u0131c\u0131 de\u011fi\u015fkenin bir yan\u0131t de\u011fi\u015fkeni \u00fczerindeki etkisini analiz ederiz.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Not<\/strong> : Bunun yerine, iki \u00f6ng\u00f6r\u00fcc\u00fc de\u011fi\u015fkenin bir yan\u0131t de\u011fi\u015fkeni \u00fczerindeki etkisiyle ilgilenseydik, <a href=\"https:\/\/statorials.org\/tr\/anova-ra-iki-yonlu\/\" target=\"_blank\" rel=\"noopener\">iki y\u00f6nl\u00fc bir ANOVA<\/a> ger\u00e7ekle\u015ftirebilirdik.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>R&#8217;de tek y\u00f6nl\u00fc ANOVA nas\u0131l ger\u00e7ekle\u015ftirilir<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">A\u015fa\u011f\u0131daki \u00f6rnek, R&#8217;de tek y\u00f6nl\u00fc ANOVA&#8217;n\u0131n nas\u0131l ger\u00e7ekle\u015ftirilece\u011fini g\u00f6stermektedir.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Arka plan<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u00dc\u00e7 farkl\u0131 egzersiz program\u0131n\u0131n kilo kayb\u0131 \u00fczerinde farkl\u0131 etkileri olup olmad\u0131\u011f\u0131n\u0131 belirlemek istedi\u011fimizi varsayal\u0131m. \u0130nceledi\u011fimiz belirleyici de\u011fi\u015fken <em>egzersiz program\u0131d\u0131r<\/em> ve <a href=\"https:\/\/statorials.org\/tr\/degiskenleri-aciklayici-yanitlar\/\" target=\"_blank\" rel=\"noopener\">yan\u0131t de\u011fi\u015fkeni<\/a> ise pound cinsinden \u00f6l\u00e7\u00fclen <em>kilo kayb\u0131d\u0131r<\/em> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u00dc\u00e7 programdan kaynaklanan kilo kayb\u0131 aras\u0131nda istatistiksel olarak anlaml\u0131 bir fark olup olmad\u0131\u011f\u0131n\u0131 belirlemek i\u00e7in tek y\u00f6nl\u00fc bir ANOVA ger\u00e7ekle\u015ftirebiliriz.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Bir ay boyunca Program A, Program B veya Program C&#8217;yi takip etmek \u00fczere rastgele 30 ki\u015fiyi atad\u0131\u011f\u0131m\u0131z bir deneye kat\u0131lmak \u00fczere 90 ki\u015fiyi i\u015fe al\u0131yoruz.<\/span><\/p>\n<p> <span style=\"color: #000000;\">A\u015fa\u011f\u0131daki kod \u00e7al\u0131\u015faca\u011f\u0131m\u0131z veri \u00e7er\u00e7evesini olu\u015fturur:<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#make this example reproducible\n<span style=\"color: #000000;\">set.seed(0)\n<\/span>\n#create data frame\n<span style=\"color: #000000;\">data &lt;- data.frame(program = rep(c(\"A\", \"B\", \"C\"), 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\n<span style=\"color: #000000;\">head(data)\n<\/span>\n<span style=\"color: #000000;\"># program weight_loss\n#1 A 2.6900916\n#2 A 0.7965260\n#3 A 1.1163717\n#4 A 1.7185601\n#5 A 2.7246234\n#6 A 0.6050458<\/span>\n<\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Veri \u00e7er\u00e7evesinin ilk s\u00fctunu ki\u015finin bir ay boyunca kat\u0131ld\u0131\u011f\u0131 program\u0131, ikinci s\u00fctun ise ki\u015finin program sonunda ya\u015fad\u0131\u011f\u0131 toplam kilo kayb\u0131n\u0131 kilo cinsinden g\u00f6stermektedir.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Verileri ke\u015ffedin<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Tek y\u00f6nl\u00fc ANOVA modelini bile yerle\u015ftirmeden \u00f6nce, <strong>dplyr<\/strong> paketini kullanarak \u00fc\u00e7 program\u0131n her biri i\u00e7in kilo kayb\u0131n\u0131n ortalamas\u0131n\u0131 ve standart sapmas\u0131n\u0131 bularak verileri daha iyi anlayabiliriz:<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load <em>dplyr<\/em> package<\/span>\n<span style=\"color: #008000;\">library<\/span> (dplyr)\n\n<span style=\"color: #008080;\">#find mean and standard deviation of weight loss for each treatment group<\/span>\ndata %&gt;%\n  <span style=\"color: #800080;\">group_by<\/span> (program) %&gt;%\n  <span style=\"color: #800080;\">summarize<\/span> (mean = mean(weight_loss),\n            sd = sd(weight_loss))\n\n# A tibble: 3 x 3\n# program mean sd\n#      \n#1 A 1.58 0.905\n#2 B 2.56 1.24 \n#3 C 4.13 1.57  \n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Ayr\u0131ca her program i\u00e7in kilo verme da\u011f\u0131l\u0131m\u0131n\u0131 g\u00f6rselle\u015ftirmek amac\u0131yla \u00fc\u00e7 program\u0131n her biri i\u00e7in bir kutu grafi\u011fi olu\u015fturabiliriz:<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create boxplots\n<\/span>boxplot(weight_loss ~ program,\ndata = data,\nmain = \"Weight Loss Distribution by Program\",\nxlab = \"Program\",\nylab = \"Weight Loss\",\ncol = \"steelblue\",\nborder = \"black\")<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Bu kutu grafiklerinden ortalama kilo kayb\u0131n\u0131n Program C&#8217;deki kat\u0131l\u0131mc\u0131lar i\u00e7in en y\u00fcksek oldu\u011funu ve ortalama kilo kayb\u0131n\u0131n Program A&#8217;daki kat\u0131l\u0131mc\u0131lar i\u00e7in en d\u00fc\u015f\u00fck oldu\u011funu g\u00f6rebiliriz.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ayr\u0131ca kilo kayb\u0131 i\u00e7in standart sapman\u0131n (kutu grafi\u011finin &#8220;uzunlu\u011fu&#8221;) C program\u0131nda di\u011fer iki programa g\u00f6re biraz daha y\u00fcksek oldu\u011funu g\u00f6rebiliriz.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Daha sonra, bu g\u00f6rsel farkl\u0131l\u0131klar\u0131n ger\u00e7ekten istatistiksel olarak anlaml\u0131 olup olmad\u0131\u011f\u0131n\u0131 g\u00f6rmek i\u00e7in tek y\u00f6nl\u00fc ANOVA modelini verilerimize uyarlayaca\u011f\u0131z.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Tek y\u00f6nl\u00fc ANOVA model uyumu<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">R&#8217;ye tek y\u00f6nl\u00fc bir ANOVA modeli yerle\u015ftirmek i\u00e7in genel s\u00f6zdizimi \u015f\u00f6yledir:<\/span><\/p>\n<p style=\"text-align: left;\"> <strong><span style=\"color: #000000;\">aov(yan\u0131t de\u011fi\u015fkeni ~ tahminci_de\u011fi\u015fken, veri = veri k\u00fcmesi)<\/span><\/strong><\/p>\n<p> <span style=\"color: #000000;\">\u00d6rne\u011fimizde, yan\u0131t de\u011fi\u015fkeni olarak <em>a\u011f\u0131rl\u0131k_kayb\u0131n\u0131<\/em> ve tahmin de\u011fi\u015fkeni olarak <em>program\u0131<\/em> kullanarak tek y\u00f6nl\u00fc ANOVA modeline uymak i\u00e7in a\u015fa\u011f\u0131daki kodu kullanabiliriz. Daha sonra modelimizin sonucunu g\u00f6r\u00fcnt\u00fclemek i\u00e7in <strong>Summary()<\/strong> fonksiyonunu kullanabiliriz:<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit the one-way ANOVA model<\/span>\nmodel &lt;- aov(weight_loss ~ program, data = data)\n\n<span style=\"color: #008080;\">#view the model output<\/span>\nsummary(model)\n\n# Df Sum Sq Mean Sq F value Pr(&gt;F)    \n#program 2 98.93 49.46 30.83 7.55e-11 ***\n#Residuals 87 139.57 1.60                     \n#---\n#Significant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Model sonu\u00e7lar\u0131ndan yorday\u0131c\u0131 de\u011fi\u015fkenler <em>program\u0131n\u0131n<\/em> istatistiksel olarak 0,05 anlaml\u0131l\u0131k d\u00fczeyinde anlaml\u0131 oldu\u011funu g\u00f6rebiliriz.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Yani \u00fc\u00e7 programdan kaynaklanan ortalama kilo kayb\u0131 aras\u0131nda istatistiksel olarak anlaml\u0131 bir fark var.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Model varsay\u0131mlar\u0131n\u0131n kontrol edilmesi<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Daha ileri gitmeden \u00f6nce, model sonu\u00e7lar\u0131m\u0131z\u0131n g\u00fcvenilir olmas\u0131 i\u00e7in modelimizin <a href=\"https:\/\/statorials.org\/tr\/danova-hipotezleri\/\" target=\"_blank\" rel=\"noopener\">varsay\u0131mlar\u0131n\u0131n<\/a> kar\u015f\u0131land\u0131\u011f\u0131n\u0131 do\u011frulamam\u0131z gerekir. \u00d6zellikle tek y\u00f6nl\u00fc bir ANOVA \u015funu varsayar:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. Ba\u011f\u0131ms\u0131zl\u0131k<\/strong> \u2013 her grubun g\u00f6zlemleri birbirinden ba\u011f\u0131ms\u0131z olmal\u0131d\u0131r.<\/span> Rastgele <span style=\"color: #000000;\">bir tasar\u0131m kulland\u0131\u011f\u0131m\u0131z i\u00e7in<\/span> <span style=\"color: #000000;\">(yani kat\u0131l\u0131mc\u0131lar\u0131 egzersiz programlar\u0131na rastgele atad\u0131\u011f\u0131m\u0131z i\u00e7in), bu varsay\u0131m\u0131n kar\u015f\u0131lanmas\u0131 gerekir ki bu konuda fazla endi\u015felenmemize gerek kalmas\u0131n.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2. Normallik<\/strong> \u2013 ba\u011f\u0131ml\u0131 de\u011fi\u015fken, yorday\u0131c\u0131 de\u011fi\u015fkenin her d\u00fczeyi i\u00e7in yakla\u015f\u0131k olarak normal bir da\u011f\u0131l\u0131ma sahip olmal\u0131d\u0131r.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>3. E\u015fit varyans<\/strong> \u2013 her grubun varyanslar\u0131 e\u015fit veya yakla\u015f\u0131k olarak e\u015fittir.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Normallik<\/strong> ve <strong>e\u015fit varyans<\/strong> varsay\u0131mlar\u0131n\u0131 kontrol etmenin bir yolu, d\u00f6rt model kontrol grafi\u011fi <strong>\u00fcretenplot()<\/strong> fonksiyonunu kullanmakt\u0131r. \u00d6zellikle a\u015fa\u011f\u0131daki iki konuyla \u00f6zellikle ilgileniyoruz:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>Art\u0131klar vs. uygun<\/strong> \u2013 bu grafik art\u0131klar ve uydurulmu\u015f de\u011ferler aras\u0131ndaki ili\u015fkiyi g\u00f6sterir. Bu grafi\u011fi, gruplar aras\u0131ndaki varyans\u0131n yakla\u015f\u0131k olarak e\u015fit olup olmad\u0131\u011f\u0131n\u0131 kabaca de\u011ferlendirmek i\u00e7in kullanabiliriz.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>QQ grafi\u011fi<\/strong> \u2013 bu grafik, standartla\u015ft\u0131r\u0131lm\u0131\u015f art\u0131klar\u0131 teorik niceliklere g\u00f6re g\u00f6sterir. Normallik varsay\u0131m\u0131n\u0131n kar\u015f\u0131lan\u0131p kar\u015f\u0131lanmad\u0131\u011f\u0131n\u0131 kabaca de\u011ferlendirmek i\u00e7in bu grafi\u011fi kullanabiliriz.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Bu model kontrol grafiklerini olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki kod kullan\u0131labilir:<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #000000;\">plot(model)<\/span><\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">Yukar\u0131daki <em>QQ grafi\u011fi<\/em> normallik varsay\u0131m\u0131n\u0131 do\u011frulamam\u0131z\u0131 sa\u011flar. \u0130deal olarak, standartla\u015ft\u0131r\u0131lm\u0131\u015f art\u0131klar grafi\u011fin d\u00fcz \u00e7apraz \u00e7izgisi boyunca uzanacakt\u0131r. Ancak yukar\u0131daki grafikte art\u0131klar\u0131n \u00e7izgiden ba\u015flang\u0131ca ve sona do\u011fru biraz sapt\u0131\u011f\u0131n\u0131 g\u00f6r\u00fcyoruz. Bu durum normallik varsay\u0131m\u0131m\u0131z\u0131n ihlal edilebilece\u011fini g\u00f6stermektedir.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><em>Kal\u0131nt\u0131lar vs.<\/em> Yukar\u0131daki <em>d\u00fczeltilmi\u015f grafik,<\/em> e\u015fit varyans varsay\u0131m\u0131m\u0131z\u0131 do\u011frulamam\u0131z\u0131 sa\u011flar. \u0130deal durumda art\u0131klar\u0131n, uygun de\u011ferlerin her d\u00fczeyi i\u00e7in e\u015fit \u015fekilde da\u011f\u0131t\u0131lmas\u0131n\u0131 isteriz.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Daha y\u00fcksek uyum de\u011ferleri i\u00e7in art\u0131klar\u0131n \u00e7ok daha fazla yay\u0131ld\u0131\u011f\u0131n\u0131 g\u00f6rebiliriz, bu da <a href=\"https:\/\/statorials.org\/tr\/esit-varyans-varsayimi\/\" target=\"_blank\" rel=\"noopener\">varyanslar\u0131n e\u015fitli\u011fi varsay\u0131m\u0131m\u0131z\u0131n<\/a> ihlal edilebilece\u011fini g\u00f6sterir.<\/span><\/p>\n<p> <span style=\"color: #000000;\">E\u015fit varyanslar\u0131 resmi olarak test etmek i\u00e7in <strong>araba<\/strong> paketini kullanarak Levene testini ger\u00e7ekle\u015ftirebiliriz:<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load car package\n<\/span><span style=\"color: #008000;\">library<\/span> (car)\n\n<span style=\"color: #008080;\">#conduct Levene's Test for equality of variances\n<\/span>leveneTest(weight_loss ~ program, data = data)\n\n#Levene's Test for Homogeneity of Variance (center = median)\n# Df F value Pr(&gt;F)  \n#group 2 4.1716 0.01862 *\n#87                  \n#---\n#Significant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Testin p de\u011feri <strong>0,01862&#8217;dir<\/strong> . E\u011fer 0,05 anlaml\u0131l\u0131k d\u00fczeyi kullan\u0131rsak, varyanslar\u0131n \u00fc\u00e7 program aras\u0131nda e\u015fit oldu\u011funa ili\u015fkin s\u0131f\u0131r hipotezini reddederiz. Ancak 0,01 anlaml\u0131l\u0131k d\u00fczeyini kullan\u0131rsak s\u0131f\u0131r hipotezini reddetmeyece\u011fiz.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Normallik ve varyanslar\u0131n e\u015fitli\u011fi varsay\u0131mlar\u0131m\u0131z\u0131n kar\u015f\u0131land\u0131\u011f\u0131ndan emin olmak i\u00e7in verileri d\u00f6n\u00fc\u015ft\u00fcrmeye \u00e7al\u0131\u015fsak da \u015fimdilik bu konuda \u00e7ok fazla endi\u015felenmeyece\u011fiz.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Tedavi farkl\u0131l\u0131klar\u0131n\u0131 analiz edin<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Model varsay\u0131mlar\u0131n\u0131n kar\u015f\u0131land\u0131\u011f\u0131n\u0131 (veya makul \u00f6l\u00e7\u00fcde kar\u015f\u0131land\u0131\u011f\u0131n\u0131) do\u011frulad\u0131ktan sonra, hangi tedavi gruplar\u0131n\u0131n birbirinden farkl\u0131 oldu\u011funu tam olarak belirlemek i\u00e7in bir post hoc testi ger\u00e7ekle\u015ftirebiliriz.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Post hoc testimizde, \u00e7oklu kar\u015f\u0131la\u015ft\u0131rmalar i\u00e7in Tukey testini ger\u00e7ekle\u015ftirmek \u00fczere <strong>TukeyHSD()<\/strong> i\u015flevini kullanaca\u011f\u0131z:<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#perform Tukey's Test for multiple comparisons\n<\/span>TukeyHSD(model, conf.level=.95) \n\n#Tukey multiple comparisons of means\n# 95% family-wise confidence level\n#\n#Fit: aov(formula = weight_loss ~ program, data = data)\n#\n#$program\n# diff lwr upr p adj\n#BA 0.9777414 0.1979466 1.757536 0.0100545\n#CA 2.5454024 1.7656076 3.325197 0.0000000\n#CB 1.5676610 0.7878662 2.347456 0.0000199\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">P de\u011feri, her program aras\u0131nda istatistiksel olarak anlaml\u0131 bir fark olup olmad\u0131\u011f\u0131n\u0131 g\u00f6sterir. Sonu\u00e7lar, her program\u0131n ortalama kilo kayb\u0131 aras\u0131nda 0,05 anlaml\u0131l\u0131k d\u00fczeyinde istatistiksel olarak anlaml\u0131 bir fark oldu\u011funu g\u00f6stermektedir.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Tukey testinden elde edilen %95 g\u00fcven aral\u0131klar\u0131n\u0131 R&#8217;deki <strong>arsa(TukeyHSD())<\/strong> fonksiyonunu kullanarak da g\u00f6rselle\u015ftirebiliriz:<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create confidence interval for each comparison\n<\/span>plot(TukeyHSD(model, conf.level=.95), las = 2)\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">G\u00fcven aral\u0131klar\u0131n\u0131n sonu\u00e7lar\u0131 hipotez testlerinin sonu\u00e7lar\u0131yla tutarl\u0131d\u0131r.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u00d6zellikle programlar aras\u0131ndaki ortalama kilo kayb\u0131na ili\u015fkin g\u00fcven aral\u0131klar\u0131n\u0131n hi\u00e7birinin <em>s\u0131f\u0131r<\/em> de\u011ferini i\u00e7ermedi\u011fini g\u00f6rebiliyoruz, bu da \u00fc\u00e7 program aras\u0131nda ortalama kilo kayb\u0131 a\u00e7\u0131s\u0131ndan istatistiksel olarak anlaml\u0131 bir fark oldu\u011funu g\u00f6steriyor.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Bu, <a href=\"https:\/\/statorials.org\/tr\/hipotez-testi-1\/\" target=\"_blank\" rel=\"noopener\">hipotez testlerimiz<\/a> i\u00e7in t\u00fcm <a href=\"https:\/\/statorials.org\/tr\/p-degerleri-istatistiksel-anlamlilik\/\" target=\"_blank\" rel=\"noopener\">p de\u011ferlerinin<\/a> 0,05&#8217;ten k\u00fc\u00e7\u00fck olmas\u0131yla tutarl\u0131d\u0131r.<\/span><\/p>\n<h3> <strong><span style=\"color: #000000;\">Tek Y\u00f6nl\u00fc ANOVA Sonu\u00e7lar\u0131n\u0131n Raporlanmas\u0131<\/span><\/strong><\/h3>\n<p> <span style=\"color: #000000;\">Son olarak tek y\u00f6nl\u00fc ANOVA sonu\u00e7lar\u0131n\u0131 \u00f6zetleyecek \u015fekilde raporlayabiliriz:<\/span><\/p>\n<p> <span style=\"color: #000000;\">Egzersiz program\u0131n\u0131n etkilerini incelemek i\u00e7in tek y\u00f6nl\u00fc ANOVA yap\u0131ld\u0131 <em>&nbsp;<\/em> kilo kayb\u0131 <em>(pound olarak \u00f6l\u00e7\u00fcl\u00fcr).<\/em> \u00dc\u00e7 program\u0131n kilo kayb\u0131 \u00fczerindeki etkileri aras\u0131nda istatistiksel olarak anlaml\u0131 bir fark vard\u0131 (F(2, 87) = 30,83, p = 7,55e-11).<\/span> <span style=\"color: #000000;\">Post-hoc Tukey&#8217;in HSD testleri yap\u0131ld\u0131.<\/span><\/p>\n<p> <span style=\"color: #000000;\">C program\u0131ndaki kat\u0131l\u0131mc\u0131lar\u0131n ortalama kilo kayb\u0131, B program\u0131ndaki kat\u0131l\u0131mc\u0131lar\u0131n ortalama kilo kayb\u0131ndan \u00f6nemli \u00f6l\u00e7\u00fcde daha y\u00fcksektir (p &lt; 0,0001).<\/span><\/p>\n<p> <span style=\"color: #000000;\">C program\u0131ndaki kat\u0131l\u0131mc\u0131lar\u0131n ortalama kilo kayb\u0131, A program\u0131ndaki kat\u0131l\u0131mc\u0131lar\u0131n ortalama kilo kayb\u0131ndan \u00f6nemli \u00f6l\u00e7\u00fcde daha y\u00fcksektir (p &lt; 0,0001).<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ek olarak, B program\u0131ndaki kat\u0131l\u0131mc\u0131lar\u0131n ortalama kilo kayb\u0131, A program\u0131ndaki kat\u0131l\u0131mc\u0131lar\u0131n ortalama kilo kayb\u0131ndan \u00f6nemli \u00f6l\u00e7\u00fcde daha fazlayd\u0131 (p = 0,01).<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Ek kaynaklar<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">A\u015fa\u011f\u0131daki e\u011fitimler tek y\u00f6nl\u00fc ANOVA&#8217;lar hakk\u0131nda ek bilgi sa\u011flar:<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/tr\/tek-yonlu-anova\/\" target=\"_blank\" rel=\"noopener\">Tek Y\u00f6nl\u00fc ANOVA&#8217;ya Giri\u015f<\/a><br \/> <a href=\"https:\/\/statorials.org\/tr\/post-hoc-anova-testleri\/\" target=\"_blank\" rel=\"noopener\">ANOVA ile Post-Hoc Testini Kullanma K\u0131lavuzu<\/a><br \/> <a href=\"https:\/\/statorials.org\/tr\/anova-sonuclari-nasil-raporlanir\/\" target=\"_blank\" rel=\"noopener\">Tam K\u0131lavuz: ANOVA Sonu\u00e7lar\u0131 Nas\u0131l Raporlan\u0131r?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\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 kullan\u0131l\u0131r. Bu t\u00fcr testlere tek y\u00f6nl\u00fc ANOVA denir \u00e7\u00fcnk\u00fc bir yorday\u0131c\u0131 de\u011fi\u015fkenin bir yan\u0131t de\u011fi\u015fkeni \u00fczerindeki etkisini analiz ederiz. Not : Bunun yerine, iki \u00f6ng\u00f6r\u00fcc\u00fc de\u011fi\u015fkenin bir yan\u0131t de\u011fi\u015fkeni \u00fczerindeki etkisiyle ilgilenseydik, iki y\u00f6nl\u00fc bir [&hellip;]<\/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":[],"class_list":["post-493","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>R - Statorials&#039;da tek y\u00f6nl\u00fc ANOVA nas\u0131l ger\u00e7ekle\u015ftirilir?<\/title>\n<meta name=\"description\" content=\"Bu e\u011fitimde, R&#039;de tek y\u00f6nl\u00fc ANOVA&#039;n\u0131n nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi tam bir \u00f6rnekle birlikte a\u00e7\u0131klanmaktad\u0131r.\" \/>\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\/tr\/tek-yonlu-anova-r\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"R - 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