Neyse ki R, plot()<\/strong> fonksiyonunu kullanarak nokta bulutlar\u0131 olu\u015fturmay\u0131 kolayla\u015ft\u0131r\u0131yor. \u00d6rne\u011fin:<\/span> <\/p>\n Abline()<\/strong> i\u015flevini kullanarak da\u011f\u0131l\u0131m grafi\u011fine bir regresyon \u00e7izgisi eklemek de kolayd\u0131r.<\/span><\/p>\n \u00d6rne\u011fin:<\/span> <\/p>\n Ayr\u0131ca tahmin()<\/strong> i\u015flevini kullanarak grafi\u011fe g\u00fcven aral\u0131\u011f\u0131 \u00e7izgileri de ekleyebiliriz:<\/span> <\/p>\n Veya bunun yerine, tahmin()<\/strong> i\u015flevinde aral\u0131k t\u00fcr\u00fcn\u00fc belirterek \u00e7izime tahmin aral\u0131\u011f\u0131 \u00e7izgileri ekleyebiliriz:<\/span> <\/p>\n Son olarak, bir ba\u015fl\u0131k ekleyerek, eksen adlar\u0131n\u0131 de\u011fi\u015ftirerek ve ayr\u0131 ayr\u0131 \u00e7izim noktalar\u0131n\u0131n \u015feklini de\u011fi\u015ftirerek \u00e7izimi estetik a\u00e7\u0131dan daha ho\u015f hale getirebiliriz.<\/span> <\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde R’de di\u011fer ortak g\u00f6revlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n R’de bir da\u011f\u0131l\u0131m grafi\u011findeki noktalar nas\u0131l etiketlenir<\/a> Genellikle basit bir do\u011frusal regresyon uygulad\u0131\u011f\u0131m\u0131zda, x ve y de\u011ferlerinin farkl\u0131 kombinasyonlar\u0131n\u0131 g\u00f6rselle\u015ftirmek i\u00e7in bir da\u011f\u0131l\u0131m grafi\u011fi olu\u015fturmak isteriz. Neyse ki R, plot() fonksiyonunu kullanarak nokta bulutlar\u0131 olu\u015fturmay\u0131 kolayla\u015ft\u0131r\u0131yor. \u00d6rne\u011fin: #create some fake data data <- data.frame(x = c(1, 1, 2, 3, 4, 4, 5, 6, 7, 7, 8, 9, 10, 11, 11), y = […]<\/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-685","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n #create some fake data<\/span>\ndata <- data.frame(x = c(1, 1, 2, 3, 4, 4, 5, 6, 7, 7, 8, 9, 10, 11, 11),\n y = c(13, 14, 17, 12, 23, 24, 25, 25, 24, 28, 32, 33, 35, 40, 41))\n\n#create scatterplot of data<\/span>\nplot(data$x, data$y)\n<\/strong><\/pre>\n![\"R'deki](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/scatterregression1.png\")
<\/p>\n #fit a simple linear regression model<\/span>\nmodel <- lm(y ~ x, data = data)\n\n#add the fitted regression line to the scatterplot<\/span>\nabline(model)<\/strong> <\/pre>\n![\"R'de](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/scatterregression2.png\")
<\/p>\n #define range of x values<\/span>\nnewx = seq(min(data$x),max(data$x),by = 1)\n\n#find 95% confidence interval for the range of x values<\/span> \nconf_interval <- predict(model, newdata=data.frame(x=newx), interval=\"confidence\",\n level = 0.95)\n\n#create scatterplot of values with regression line<\/span>\nplot(data$x, data$y)\nabline(model)\n\n#add dashed lines (lty=2) for the 95% confidence interval<\/span>\nlines(newx, conf_interval[,2], col=\"blue\", lty=2)\nlines(newx, conf_interval[,3], col=\"blue\", lty=2)\n<\/strong><\/pre>\n![\"R](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/scatterregression3.png\")
<\/p>\n #define range of x values<\/span>\nnewx = seq(min(data$x),max(data$x),by = 1)\n\n#find 95% prediction interval for the range of x values<\/span> \npred_interval <- predict(model, newdata=data.frame(x=newx), interval=\"prediction\"<\/span> ,\n level = 0.95)\n\n#create scatterplot of values with regression line<\/span>\nplot(data$x, data$y)\nabline(model)\n\n#add dashed lines (lty=2) for the 95% confidence interval<\/span>\nlines(newx, pred_interval[,2], col=\"red\", lty=2)\nlines(newx, pred_interval[,3], col=\"red\", lty=2)\n<\/strong><\/pre>\n![\"R'de](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/scatterregression4.png\")
<\/p>\n plot(data$x, data$y,\n main = \"Scatterplot of x vs. y\", #add title<\/span>\n pch=16, #specify points to be filled in<\/span>\n xlab='x', #change x-axis name\n<\/span>ylab='y') #change y-axis name<\/span>\n\nabline(model, col='steelblue') #specify color of regression line<\/span><\/span>\n<\/strong><\/pre>\n![\"R'de](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/scatterregression5.png\")
<\/p>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Nokta bulutlar\u0131 i\u00e7in R’de Jitter i\u015flevi nas\u0131l kullan\u0131l\u0131r?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"