However - for a large n and a sufficiently large stddev, you are pretty much guaranteed to get some samples less than xmin and some greater than xmax. The result p is the probability that a single observation from the t distribution with degrees of freedom falls in the interval. p F ( x ) x ( + 1 2) ( 2) 1 1 ( 1 + t 2 ) + 1 2 d t, where is the degrees of freedom and ( ) is the Gamma function. It shows you the percent of population: between 0 and Z (option '0 to Z') less than Z (option 'Up to Z') greater than Z (option 'Z onwards') It only display values to 0.01. > out = xmin + ( (xmean-xmin) + stddev * randn(1,n) ) Īnd you can verify that mean(out) and std(out) are approximately 150 and 25, respectively. The cdf of the Student’s t distribution is. It is a Normal Distribution with mean 0 and standard deviation 1.
If you expect the mean to be, say, 150 and the standard deviation to be 25, you would accomplish this as follows: > stddev = 25
To change the mean of this distribution to an arbitrary x and the standard deviation to y, simply do x + y*randn(1,n). First, you'll want to use randn for a normal distribution - rand will draw from a uniform distribution.Ĭalling randn(1,n) will return n normally distributed samples from the standard distribution with mean of zero and a variance (standard deviation squared) of one.
