Generation of

Normally Distributed Random Numbers (only integers)

* 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. |