448
Comment:

859

Deletions are marked like this.  Additions are marked like this. 
Line 1:  Line 1: 
= 
= How do I produce random variables which follow a negative skew distribution? = 
Line 8:  Line 8: 
The below produces negatively skewed data with no upper bound. {{{ define !gamma ( !pos !tokens(1) /!pos !tokens(1)). !do !i=!1 !to !2 !by 1. compute !concat(a,!i)=(10000)*ln(rv.uniform(0,1)*10000). !doend. !enddefine. !gamma 1 2. exe. compute sum=0. exe. compute sum=(a1+a2). exe. }}} 
How do I produce random variables which follow a negative skew distribution?
Most distributions such as the exponential and logNormal distributions are positive skewed with the model of the distribution for lower values.
[http://www.uib.no/people/ngbnk/kurs/notes/node31.html The Gamma distribution] which has two parameters, $$\alpha$$ and $$\beta$$ may produce negative skew where the model occurs for higher values (values > 0) when $$/alpha$$ is a lot greater than $$\beta$$. It also has no maximum value.
The below produces negatively skewed data with no upper bound.
define !gamma ( !pos !tokens(1) /!pos !tokens(1)). !do !i=!1 !to !2 !by 1. compute !concat(a,!i)=(10000)*ln(rv.uniform(0,1)*10000). !doend. !enddefine. !gamma 1 2. exe. compute sum=0. exe. compute sum=(a1+a2). exe.