Plots the CDF and PDF graphs for normal distribution with given mean and variance.
Normal distribution takes a unique role in the probability theory. This is the most common continuous probability distribution, commonly used for random values representation of unknown distribution law.
Probability density function
Normal distribution probability density function is the Gauss function:
where μ — mean,
σ — standard deviation,
σ ² — variance,
Median and mode of Normal distribution equal to mean μ.
The calculator below gives probability density function value and cumulative distribution function value for the given x, mean, and variance:
Cumulative distribution function
Normal distribution cumulative distribution function has the following formula:
where, erf(x) - error function, given as:
Normal distribution quantile function (inverse CDF) given as inverse error function:
p lays in the range [0,1]
Standard normal distribution quantile function (σ =1, μ=0) equates like this:
This function is called the probit function.
The calculator below gives quantile value by probability for the specified through mean and variance normal distribution( set variance=1 and mean=0 for probit function).