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.