In fact, setting the seed too often can actually reduce the quality of the random numbers! If you don’t know that, then read help set seed in Stata. You set the seed not to obtain better random numbers, but to obtain reproducible random numbers. That is not required but it is recommended. You should already know that you can set the random-number seed before using the generators. This series is not about that, so we’ll be relaxed about calling them random-number generators.

You already know that these random-number generators do not really produce random numbers they produce pseudo-random numbers.

rt( df ) generates Student’s t( df) random numbers.

rpoisson( m ) generates Poisson( m) random numbers.

rnormal( μ, σ ) generates Gaussian normal random numbers.

rnbinomial( n, p ) generates negative binomial - the number of failures before the nth success - random numbers, where p is the probability of a success.

rhypergeometric( N, K, n ) generates hypergeometric random numbers, where N is the population size, K is the number of in the population having the attribute of interest, and n is the sample size.rgamma( a, b ) generates Γ( a, b) random numbers, where a is the shape parameter and b, the scale parameter.rchi2( df ) generates χ 2 with df degrees of freedom random numbers.rbinomial( n, p ) generates binomial( n, p) random numbers, where n is the number of trials and p the probability of a success.rbeta( a, b ) generates beta-distribution beta( a, b) random numbers.runiform() generates rectangularly (uniformly) distributed random number over [0,1).Stata in fact has ten random-number functions: I want to start a series on using Stata’s random-number function.

Generate 50 random numbers