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The Gamma distribution[http://www.aiaccess.net/English/Glossaries/GlosMod/e_gm_gamma_distri.htm may be produced by summing exponential distributions.] There are two parameters, n and $$\lambda$$. n is the number of summed exponentials and $$\lambda$$ is the exponential rate. When $$\lambda$$ is very small compared to n a negative skewed version of the gamma distribution results with no upper limit.  The Gamma distribution[http://www.aiaccess.net/English/Glossaries/GlosMod/e_gm_gamma_distri.htm:may be produced by summing exponential distributions.] There are two parameters, n and $$\lambda$$. n is the number of summed exponentials and $$\lambda$$ is the exponential rate. When $$\lambda$$ is very small compared to n a negative skewed version of the gamma distribution results with no upper limit. 
How do I produce random variables which follow a negatively skewed distribution?
Most distributions such as the exponential and logNormal distributions are positively skewed with the mode of the distribution occurring for lower values.
The Gamma distribution[http://www.aiaccess.net/English/Glossaries/GlosMod/e_gm_gamma_distri.htm:may be produced by summing exponential distributions.] There are two parameters, n and $$\lambda$$. n is the number of summed exponentials and $$\lambda$$ is the exponential rate. When $$\lambda$$ is very small compared to n a negative skewed version of the gamma distribution results with no upper limit.
The below produces two exponential random variables (n=2) with a very small $$\lambda$$ (=1/10000) in SPSS. One simulated data set produced using this macro had a skew of 1.03.
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.