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:
- Model/function choice: hypothesize families of distributions;
- Estimate parameters;
- Evaluate quality of fit;
- 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.