Normal probability distribution object
A NormalDistribution
object consists of parameters, a model
description, and sample data for a normal probability distribution.
The normal distribution, sometimes called the Gaussian distribution, is a two-parameter family of curves. The usual justification for using the normal distribution for modeling is the Central Limit theorem, which states (roughly) that the sum of independent samples from any distribution with finite mean and variance converges to the normal distribution as the sample size goes to infinity.
The normal distribution uses the following parameters.
Parameter | Description | Support |
---|---|---|
mu (μ) | Mean | |
sigma (σ) | Standard deviation |
There are several ways to create a NormalDistribution
probability
distribution object.
Create a distribution with specified parameter values using makedist
.
Fit a distribution to data using fitdist
.
Interactively fit a distribution to data using the Distribution Fitter app.
cdf | Cumulative distribution function |
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 |