Random Numbers from Normal Distribution with Specific Mean and Variance
This example shows how to create an array of random floating-point numbers that are drawn from a normal distribution having a mean of 500 and variance of 25.
randn function returns a sample of
random numbers from a normal distribution with mean 0 and variance
1. The general theory of random variables states that if x is
a random variable whose mean is and
variance is ,
then the random variable, y, defined by where a and b are
constants, has mean and
can apply this concept to get a sample of normally distributed random
numbers with mean 500 and variance 25.
First, initialize the random number generator to make the results in this example repeatable.
Create a vector of 1000 random values drawn from a normal distribution with a mean of 500 and a standard deviation of 5.
a = 5; b = 500; y = a.*randn(1000,1) + b;
Calculate the sample mean, standard deviation, and variance.
stats = [mean(y) std(y) var(y)]
stats = 1×3 499.8368 4.9948 24.9483
The mean and variance are not 500 and 25 exactly because they are calculated from a sampling of the distribution.