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

 NormalDistribution Normal probability distribution object

Apps

 Distribution Fitter Fit probability distributions to data Probability Distribution Function Interactive density and distribution plots

Functions

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Create NormalDistribution Object

 makedist Create probability distribution object fitdist Fit probability distribution object to data

Work with NormalDistribution Object

 cdf Cumulative distribution function gather Gather properties of Statistics and Machine Learning Toolbox object from GPU icdf Inverse cumulative distribution function iqr Interquartile range mean Mean of probability distribution median Median of probability distribution negloglik Negative loglikelihood of probability distribution paramci Confidence intervals for probability distribution parameters pdf Probability density function 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 loglikelihood normstat Normal mean and variance normfit Normal parameter estimates normrnd Normal random numbers
 mle Maximum likelihood estimates mlecov Asymptotic covariance of maximum likelihood estimators
 histfit Histogram with a distribution fit normplot Normal probability plot normspec Normal density plot shading between specifications qqplot Quantile-quantile plot randtool Interactive 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.