Generation of

Normally Distributed Random Numbers (only integers)

 Mean : μ Standard Deviation :     σ First Random Number : G Final Random Number: R Box-Muller Transform : Random Number1: R2a Random Number1: R2b
 * We have a collection of data from which we calculate Mean & Standard Deviation. If mean=mode=median and about 68% of the data are within mean+1SD, 95% of the data within mean+2SD and 99% of the data within  mean+3SD, then as per thumb rule, the distribution is normal or Gaussian. * What is the opposite? A mean is given, a standard deviation is given and we have to generate data that will follow normal distribution. Cumulative multiple random values simulate normal distribution and hence we generate a random number which is sum of 3 random numbers between -1 to +1 This will give a normal distribution with mean zero and standard deviation 1. This is called Normal Standard Deviation. * Then we generate R with R = Gσ +  μ    and round it up. Example- Generate a fantasy Bust Size with mean 36 and SD=2 for a fantasy babe. References: 1,2, 3, 4,

Box-Muller Transform

 *This function generates pairs of standard normal distribution random numbers from uniform distribution random numbers. It is a Pseudo-random number sampling method.