<\/p>\n
Genellikle CV<\/em> olarak k\u0131salt\u0131lan bir varyasyon katsay\u0131s\u0131<\/strong> , bir veri k\u00fcmesindeki de\u011ferlerin ortalamaya g\u00f6re yay\u0131lmas\u0131n\u0131 \u00f6l\u00e7menin bir yoludur. A\u015fa\u011f\u0131daki \u015fekilde hesaplan\u0131r:<\/span><\/p>\n CV = \u03c3 \/ \u03bc<\/span><\/strong><\/p>\n Alt\u0131n:<\/span><\/p>\n Basit\u00e7e s\u00f6ylemek gerekirse, varyasyon katsay\u0131s\u0131 standart sapman\u0131n ortalamaya oran\u0131d\u0131r.<\/span><\/p>\n De\u011fi\u015fim katsay\u0131s\u0131 genellikle iki farkl\u0131 veri k\u00fcmesi aras\u0131ndaki de\u011fi\u015fimi kar\u015f\u0131la\u015ft\u0131rmak i\u00e7in kullan\u0131l\u0131r.<\/span><\/p>\n Ger\u00e7ek d\u00fcnyada, finansta bir yat\u0131r\u0131m\u0131n ortalama beklenen getirisini, yat\u0131r\u0131m\u0131n beklenen standart sapmas\u0131 ile kar\u015f\u0131la\u015ft\u0131rmak i\u00e7in s\u0131kl\u0131kla kullan\u0131l\u0131r. Bu, yat\u0131r\u0131mc\u0131lar\u0131n yat\u0131r\u0131mlar aras\u0131ndaki risk-getiri dengesini kar\u015f\u0131la\u015ft\u0131rmas\u0131na olanak tan\u0131r.<\/span><\/p>\n \u00d6rne\u011fin, bir yat\u0131r\u0131mc\u0131n\u0131n a\u015fa\u011f\u0131daki iki yat\u0131r\u0131m fonuna yat\u0131r\u0131m yapmay\u0131 d\u00fc\u015f\u00fcnd\u00fc\u011f\u00fcn\u00fc varsayal\u0131m:<\/span><\/p>\n Yat\u0131r\u0131m Fonu A: ortalama = %9, standart sapma = %12,4<\/span><\/p>\n UCITS B: ortalama = %5, standart sapma = %8,2<\/span><\/p>\n Yat\u0131r\u0131mc\u0131, her fonun de\u011fi\u015fim katsay\u0131s\u0131n\u0131 hesaplayarak \u015funlar\u0131 not eder:<\/span><\/p>\n Yat\u0131r\u0131m fonu A i\u00e7in CV = %12,4 \/%9 = 1,38<\/strong><\/span><\/p>\n Yat\u0131r\u0131m fonu B i\u00e7in CV = %8,2 \/ %5 = 1,64<\/strong><\/span><\/p>\n A Yat\u0131r\u0131m Fonu’nun de\u011fi\u015fim katsay\u0131s\u0131 daha d\u00fc\u015f\u00fck oldu\u011fundan standart sapmaya g\u00f6re daha iyi bir ortalama getiri sa\u011flamaktad\u0131r.<\/span><\/p>\n R’deki bir veri k\u00fcmesinin varyasyon katsay\u0131s\u0131n\u0131 hesaplamak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekler bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6stermektedir.<\/span><\/p>\n \u00d6rnek 1: Tek bir vekt\u00f6r i\u00e7in varyasyon katsay\u0131s\u0131<\/strong><\/span><\/p>\n A\u015fa\u011f\u0131daki kod tek bir vekt\u00f6r i\u00e7in CV’nin nas\u0131l hesaplanaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n De\u011fi\u015fim katsay\u0131s\u0131 9,23<\/strong> olarak \u00e7\u0131k\u0131yor.<\/span><\/p>\n \u00d6rnek 2: \u00c7e\u015fitli vekt\u00f6rler i\u00e7in varyasyon katsay\u0131s\u0131<\/strong><\/span><\/p>\n A\u015fa\u011f\u0131daki kod, sapply()<\/a> i\u015flevini kullanarak bir veri \u00e7er\u00e7evesindeki birden fazla vekt\u00f6r\u00fcn CV’sinin nas\u0131l hesaplanaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Verilerinizde de de\u011ferler eksikse na.rm=T<\/strong> kulland\u0131\u011f\u0131n\u0131zdan emin olun. Bu, R’ye varyasyon katsay\u0131s\u0131n\u0131 hesaplarken eksik de\u011ferleri g\u00f6z ard\u0131 etmesini s\u00f6yler:<\/span><\/p>\n R’de mutlak medyan sapma nas\u0131l hesaplan\u0131r<\/a> Genellikle CV olarak k\u0131salt\u0131lan bir varyasyon katsay\u0131s\u0131 , bir veri k\u00fcmesindeki de\u011ferlerin ortalamaya g\u00f6re yay\u0131lmas\u0131n\u0131 \u00f6l\u00e7menin bir yoludur. A\u015fa\u011f\u0131daki \u015fekilde hesaplan\u0131r: CV = \u03c3 \/ \u03bc Alt\u0131n: \u03c3: veri k\u00fcmesinin standart sapmas\u0131 \u03bc: veri setinin ortalamas\u0131 Basit\u00e7e s\u00f6ylemek gerekirse, varyasyon katsay\u0131s\u0131 standart sapman\u0131n ortalamaya oran\u0131d\u0131r. De\u011fi\u015fim katsay\u0131s\u0131 ne zaman kullan\u0131l\u0131r? De\u011fi\u015fim katsay\u0131s\u0131 genellikle iki farkl\u0131 […]<\/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-1321","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n\n
De\u011fi\u015fim katsay\u0131s\u0131 ne zaman kullan\u0131l\u0131r?<\/strong><\/span><\/h3>\n
R’nin varyasyon katsay\u0131s\u0131 nas\u0131l hesaplan\u0131r?<\/strong><\/span><\/h3>\n
cv <- sd(data) \/ mean(data) * 100\n<\/span><\/strong><\/pre>\n #create vector of data\n<\/span>data <- c(88, 85, 82, 97, 67, 77, 74, 86, 81, 95, 77, 88, 85, 76, 81, 82)\n\n#calculate CV\n<\/span>cv <- sd(data) \/ mean(data) * 100<\/span>\n\n#display CV\n<\/span>resume\n\n[1] 9.234518\n<\/strong><\/pre>\n #create data frame\n<\/span>data <- data.frame(a=c(88, 85, 82, 97, 67, 77, 74, 86, 81, 95),\n b=c(77, 88, 85, 76, 81, 82, 88, 91, 92, 99),\n c=c(67, 68, 68, 74, 74, 76, 76, 77, 78, 84))\n\n#calculate CV for each column in data frame\nsapply(data, function<\/span> (x) sd(x) \/ mean(x) * 100<\/span> )\n\n ABC\n11.012892 8.330843 7.154009<\/span><\/span><\/strong><\/pre>\n #create data frame\n<\/span>data <- data.frame(a=c(88, 85, 82, 97, 67, 77, 74, 86, 81, 95),\n b=c(77, 88, 85, 76, 81, 82, 88, 91, NA<\/span> , 99),\n c=c(67, 68, 68, 74, 74, 76, 76, 77, 78, NA<\/span> ))\n\n#calculate CV for each column in data frame\nsapply(data, function<\/span> (x) sd(x, na.rm= T<\/span> ) \/ mean(x, na.rm= T<\/span> ) * 100<\/span> )\n\n ABC\n11.012892 8.497612 5.860924\n<\/span><\/span><\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
R’de standart sapma nas\u0131l hesaplan\u0131r<\/a>
R’de aral\u0131k nas\u0131l bulunur?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"