Although the normal distribution is a reasonable input distribution in many situations, it does have two potential

Question:

Although the normal is a reasonable input in many situations, it does have two potential drawbacks:
(1) It allows negative values, even though they may be extremely improbable, and
(2) It is a symmetric
Many situations are modeled better with a that allows only positive values and is skewed to the right. Two of these that have been used in many real applications are the gamma and lognormal distributions. @RISK enables you to generate observations from each of these distributions.
The @RISK function for the gamma is RISKGAMMA, and it takes two arguments, as in =RISKGAMMA(3,10). The first argument, which must be positive, determines the shape. The smaller it is, the more skewed the is to the right; the larger it is, the more symmetric the is. The second argument determines the scale, in the sense that the product of it and the first argument equals the mean of the Also, the product of the second argument and the square root of the first argument is the standard deviation of the The @RISK function for the lognormal is RISKLOGNORM. It has two arguments, as in =RISKLOGNORM(40,10).
These arguments are the mean and standard deviation of the Rework Example 15.2 for the following demand distributions. Do the simulated outputs have any different qualitative properties with these skewed distributions than with the triangular used in the example?
a. Gamma with parameters 2 and 85
b. Gamma with parameters 5 and 35
c. Lognormal with mean 170 and standard deviation 60

Distribution
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