Probability and Statistics
Probability Distributions Guide
With an application in R
Probability Distributions
Probability distributions are mathematical functions that provide the probabilities of occurrence of different possible outcomes in a given experiment. They come in many different shapes with different characteristics, as defined by a mean, standard deviation, skewness, and kurtosis.
Throughout this article, 16 probability distributions are explained with their respective parameters, as well with the R code to build them.
Normal Distribution
Description:
The normal distribution, also known as the Gaussian distribution or bell curve, is the most important probability distribution for continuous variables since it occurs in many many situations. It is determined by two parameters, the mean (which coincides with the median and the mode) and the variance. The relevance of the normal distribution is due to the central limit theorem, which states that the sum of n random variables (regardless of its mean, variance, and distribution) approximates a normal distribution as n increases.
Parameters:
- μ: mean
