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.
NormalDistribution | Normal probability distribution object |
Distribution Fitter | Fit probability distributions to data |
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.