Thetype exposes the following members.
Gets the number of degrees of freedom ν of the distribution.
Gets the excess kurtosis of the distribution.(Overrides UnivariateDistributionExcessKurtosis.)
Gets the mean of the distribution.(Overrides UnivariateDistributionMean.)
Gets the median of the distribution.(Overrides DistributionMedian.)
Gets the skewness of the distribution.(Overrides UnivariateDistributionSkewness.)
Gets the standard deviation of the distribution.(Inherited from UnivariateDistribution.)
Gets the interval over which the distribution is nonvanishing.(Overrides DistributionSupport.)
Gets the variance of the distribution.(Overrides UnivariateDistributionVariance.)
Computes a cumulant of the distribution.(Overrides UnivariateDistributionCumulant(Int32).)
Computes the expectation value of the given function.(Inherited from Distribution.)
Serves as a hash function for a particular type.(Inherited from Object.)
Returns a random value.(Inherited from Distribution.)
Gets the Type of the current instance.(Inherited from Object.)
Returns the point at which the cumulative distribution function attains a given value.(Overrides DistributionInverseLeftProbability(Double).)
Returns the point at which the right probability function attains the given value.(Inherited from Distribution.)
Returns the cumulative probability to the left of (below) the given point.(Overrides DistributionLeftProbability(Double).)
Computes a raw moment of the distribution.(Overrides DistributionMoment(Int32).)
Computes a central moment of the distribution.(Overrides DistributionMomentAboutMean(Int32).)
Returns the probability density at the given point.(Overrides DistributionProbabilityDensity(Double).)
Return the cumulative probability to the right of (above) the given point.(Overrides DistributionRightProbability(Double).)
Returns a string that represents the current object.(Inherited from Object.)
A chi squared distribution is an asymmetrical distribution ranging from zero to infinity with a peak near its number of degrees of freedom ν. It is a one-parameter distribution determined entirely by the parameter nu.
The figure above shows the χ2 distribution for ν = 6, as well as the normal distribution with equal mean and variance for reference.
The sum of the squares of ν independent standard-normal distributed variables is distributed as χ2 with ν degrees of freedom.
The χ2 distribution appears in least-squares fitting as the distribution of the sum-of-squared-deviations under the null hypothesis that the model explains the data. For example, the goodness-of-fit statistic returned by the model our model fitting methods (FitToFunction(FuncDouble, T, Double, Double), FitToLinearFunction(FuncT, Double), FitToLine, and others) follows a χ2 distribution.