Normal Distribution

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

Statistics and Machine Learning Toolbox™ offers several ways to work with the normal distribution.

  • Create a probability distribution object NormalDistribution by fitting a probability distribution to sample data or by specifying parameter values. Then, use object functions to evaluate the distribution, generate random numbers, and so on.

  • Work with the normal distribution interactively by using the Distribution Fitter app. You can export an object from the app and use the object functions.

  • Use distribution-specific functions with specified distribution parameters. The distribution-specific functions can accept parameters of multiple normal distributions.

  • Use generic distribution functions (cdf, icdf, pdf, random) with a specified distribution name ('Normal') and parameters.

To learn about the normal distribution, see Normal Distribution.

Objects

NormalDistributionNormal probability distribution object

Apps

Distribution FitterFit probability distributions to data

Functions

expand all

Create NormalDistribution Object

makedistCreate probability distribution object
fitdistFit probability distribution object to data

Work with NormalDistribution Object

cdfCumulative distribution function
icdfInverse cumulative distribution function
iqrInterquartile range
meanMean of probability distribution
medianMedian of probability distribution
negloglikNegative loglikelihood of probability distribution
paramciConfidence intervals for probability distribution parameters
pdfProbability density function
proflikProfile likelihood function for probability distribution
randomRandom numbers
stdStandard deviation of probability distribution
truncateTruncate probability distribution object
varVariance of probability distribution
normcdfNormal cumulative distribution function
normpdfNormal probability density function
norminvNormal inverse cumulative distribution function
normlikeNormal negative loglikelihood
normstatNormal mean and variance
normfitNormal parameter estimates
normrndNormal random numbers
mleMaximum likelihood estimates
mlecovAsymptotic covariance of maximum likelihood estimators
histfitHistogram with a distribution fit
normplotNormal probability plot
normspecNormal density plot shading between specifications
Probability Distribution FunctionInteractive density and distribution plots
qqplotQuantile-quantile plot
randtoolInteractive random number generation

Topics

Normal Distribution

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