n this post, we show how to generate random numbers into vector and matrix in R programming, from various statistical distributions. Specifically, we focus several basic and widely used statistical distributions here, namely, normal distribution, continuous uniform distribution, binomial distribution and Poisson distribution.
Normal distribution is describing random variables with bell-shaped probability density functions. Normal distribution is widely used in data science because large sample random variates have a mean value which follows approximate normal distribution if variates are independently drawn from any distributions. The probability density function for normal distribution is determined by two parameters: mean(miu) and standard deviation(sigma).
Lognormal distribution in probability and statistics is used to model the distribution of a positive random variable Y, if Y = ln(X) has a normal distribution with mean μ and standard deviation σ.