# Documentation

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# Normal Distribution

Fit, evaluate, and generate random samples from normal (Gaussian) distribution

## Functions

 `makedist` Create probability distribution object `fitdist` Fit probability distribution object to data `dfittool` Open Distribution Fitting app
 `cdf` Cumulative distribution functions `icdf` Inverse cumulative distribution functions `iqr` Interquartile range `mean` Mean of probability distribution `median` Median of probability distribution `negloglik` Negative log likelihood of probability distribution `paramci` Confidence intervals for probability distribution parameters `pdf` Probability density functions `proflik` Profile likelihood function for probability distribution `random` Random numbers `std` Standard deviation of probability distribution `truncate` Truncate probability distribution object `var` Variance of probability distribution
 `normcdf` Normal cumulative distribution function `normpdf` Normal probability density function `norminv` Normal inverse cumulative distribution function `normlike` Normal negative log-likelihood `normstat` Normal mean and variance `normfit` Normal parameter estimates `normrnd` Normal random numbers `random` Random numbers

## Using Objects

 `NormalDistribution` Normal probability distribution object

## Examples and How To

Compare Multiple Distribution Fits

Fit multiple probability distribution objects to the same set of sample data, and obtain a visual comparison of how well each distribution fits the data.

## Concepts

Normal Distribution

The normal distribution is a two-parameter (mean and standard deviation) family of curves. Central Limit Theorem states that it models the sum of independent samples from any distribution as the sample size goes to infinity.