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Normal Distribution

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


makedistCreate probability distribution object
fitdistFit probability distribution object to data
distributionFitterOpen Distribution Fitter app
cdfCumulative distribution functions
icdfInverse cumulative distribution functions
iqrInterquartile range
meanMean of probability distribution
medianMedian of probability distribution
negloglikNegative log likelihood of probability distribution
paramciConfidence intervals for probability distribution parameters
pdfProbability density functions
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 log-likelihood
normstatNormal mean and variance
normfitNormal parameter estimates
normrndNormal random numbers

Using Objects

NormalDistributionNormal 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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