In Chapters 6 and 11, we will discuss more properties of the gamma random variables. Gamma distribution. A Gamma random variable is a sum of squared normal random variables. 0 If we divide both sides by ( ) we get 1 1 = x −1e −xdx = y e ydy 0 0 In the lecture entitled Chi-square distribution we have explained that a Chi-square random variable with degrees of freedom (integer) can be written as a sum of squares of independent normal random variables , ..., having mean and variance :. Gamma function ( ) is defined by ( ) = x −1e−xdx. Let us take two parameters > 0 and > 0. Here, we will provide an introduction to the gamma distribution. The gamma distribution is another widely used distribution. There are two ways to determine the gamma distribution mean. Gamma distribution, 2-distribution, Student t-distribution, Fisher F -distribution. Gamma Distribution Variance. Directly; Expanding the moment generation function; It is also known as the Expected value of Gamma Distribution. It can be shown as follows: So, Variance = E[x 2] – [E(x 2)], where p = (E(x)) (Mean and Variance p(p+1) – p 2 = p Its importance is largely due to its relation to exponential and normal distributions. Gamma Distribution Mean.