SiegelTukeyTest (Built-in Mathematica Symbol)

SiegelTukeyTest[{data_1, data_2}] tests whether the variances of data_1 and data_2 are equal.SiegelTukeyTest[dspec, \[Sigma]_0^2] tests a dispersion measure against ...

SkewNormalDistribution (Built-in Mathematica Symbol)

SkewNormalDistribution[\[Mu], \[Sigma], \[Alpha]] represents a skew-normal distribution with shape parameter \[Alpha], location parameter \[Mu], and scale parameter \[Sigma].

SymletWavelet (Built-in Mathematica Symbol)

SymletWavelet[] represents the Symlet wavelet of order 4.SymletWavelet[n] represents the Symlet wavelet of order n.

SystemsModelParallelConnect (Built-in Mathematica Symbol)

SystemsModelParallelConnect[sys_1, sys_2] connects TransferFunctionModel or StateSpaceModel objects sys_1 and sys_2 in parallel.SystemsModelParallelConnect[sys_1, sys_2, ...

SystemsModelSeriesConnect (Built-in Mathematica Symbol)

SystemsModelSeriesConnect[sys_1, sys_2] connects TransferFunctionModel or StateSpaceModel objects sys_1 and sys_2 in series. SystemsModelSeriesConnect[sys_1, sys_2, {y, u}] ...

TukeyLambdaDistribution (Built-in Mathematica Symbol)

TukeyLambdaDistribution[\[Lambda]] represents Tukey's lambda distribution with shape parameter \[Lambda].TukeyLambdaDistribution[\[Lambda], \[Mu], \[Sigma]] represents ...

UniformSumDistribution (Built-in Mathematica Symbol)

UniformSumDistribution[n] represents the distribution of a sum of n random variables uniformly distributed from 0 to 1.UniformSumDistribution[n, {min, max}] represents the ...

WatsonUSquareTest (Built-in Mathematica Symbol)

WatsonUSquareTest[data] tests whether data is normally distributed using the Watson U^2 test.WatsonUSquareTest[data, dist] tests whether data is distributed according to dist ...

Get Results for Fitted Models (Mathematica How To)

When fitting data to a model, it is often important to obtain additional results to compare the data to the fitted function. You may wish to check the significance of ...

Plot Diagnostics for Fitted Models (Mathematica How To)

Diagnostics are an important part of analyzing models of data. Plots of residuals and leverage or influence measures provide valuable insight into whether assumptions of the ...