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In “Fitting Distributions with R” Vito Ricci writes;

“Fitting distributions consists in finding a mathematical function which represents in a good way a statistical variable. A statistician often is facing with this problem: he has some observations of a quantitative character $x_1, x_2, …, x_n$ and he wishes to test if those observations, being a sample of an unknown population, belong from a population with a pdf (probability density function) $\ f(x,\theta)$, where $\ \theta$ is a vector of parameters to estimate with available data.

We can identify 4 steps in fitting distributions:

  1. Model/function choice: hypothesize families of distributions;
  2. Estimate parameters;
  3. Evaluate quality of fit;
  4. Goodness of fit statistical tests.”

In SAS this can be done by using proc capability whereas in R we can do the same thing by using fdistrplus and some other packages. In this post I will try to compare the procedures in R and SAS.

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I was looking for a way to include the Git revision numbers to my $\rm \LaTeX$ files and found Brent Longborough’s gitinfo package which is available on CTAN. Here’s the definition from README;

The gitinfo package provides to allow version control metadata to be incorporated into documents. The metadata is obtained from the git distributed version control system.

I followed the setup steps explained in the documentation but faced a problem during the setup. I realized that in order to succesfully create the gitHeadinfo.gin, the files created in the hooks folder must be executables.

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En Küçük Kareler (EKK) regresyonu ekonometrik analizin çekirdeğidir. Bu bölümde tek değişkenli ve çok değişkenli regresyon tahminleri yapılarak, R çıktısında yer alan regresyon istatistiklerinin tanımlamaları yapılmaktadır.

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R’da basit regresyon analizini gerçekleştirmek için öncelikle aşağıdaki kod girilerek gerekli kitaplık yüklenmelidir.


Analizde kullanılan veri seti 20 gözlem ve 2 değişken (Boy (X) ve Kilo (Y)) içermektedir. Veriyi R’a aktarmak için aşağıda yer alan komutlar kullanılmalıdır.

url <- ""
data <- getURL(url)
data <- read.csv(textConnection(data))
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