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Normal Distribution

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


makedist Create probability distribution object
fitdist Fit probability distribution object to data
distributionFitter Open Distribution Fitter 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

Using Objects

NormalDistribution Normal probability distribution object

Examples and How To

Compare Multiple Distribution Fits

This example shows how to 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.


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.

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