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betastat


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 -- statistics: [M, V] = betastat (A, B)

     Compute statistics of the Beta distribution.

     ‘[M, V] = betastat (A, B)’ returns the mean and variance of the Beta
     distribution with shape parameters A and B.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Further information about the Beta distribution can be found at
     <https://en.wikipedia.org/wiki/Beta_distribution>

     See also: betacdf, betainv, betapdf, betarnd, betafit, betalike.


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Compute statistics of the Beta distribution.



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binostat


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 -- statistics: [M, V] = binostat (N, PS)

     Compute statistics of the binomial distribution.

     ‘[M, V] = binostat (N, PS)’ returns the mean and variance of the binomial
     distribution with parameters N and PS, where N is the number of trials and
     PS is the probability of success.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Further information about the binomial distribution can be found at
     <https://en.wikipedia.org/wiki/Binomial_distribution>

     See also: binocdf, binoinv, binopdf, binornd, binofit, binolike, binotest.


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Compute statistics of the binomial distribution.



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bisastat


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 -- statistics: [M, V] = bisastat (BETA, GAMMA)

     Compute statistics of the Birnbaum-Saunders distribution.

     ‘[M, V] = bisastat (BETA, GAMMA)’ returns the mean and variance of the
     Birnbaum-Saunders distribution with scale parameter BETA and shape
     parameter GAMMA.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Further information about the Birnbaum-Saunders distribution can be found
     at <https://en.wikipedia.org/wiki/Birnbaum%E2%80%93Saunders_distribution>

     See also: bisacdf, bisainv, bisapdf, bisarnd, bisafit, bisalike.


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Compute statistics of the Birnbaum-Saunders distribution.



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burrstat


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 -- statistics: [M, V] = burrstat (LAMBDA, C, K)

     Compute statistics of the Burr type XII distribution.

     ‘[M, V] = burrstat (LAMBDA, C, K)’ returns the mean and variance of the
     Burr type XII distribution with scale parameter LAMBDA, first shape
     parameter C, and second shape parameter K.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Further information about the Burr distribution can be found at
     <https://en.wikipedia.org/wiki/Burr_distribution>

     See also: gevcdf, gevinv, gevpdf, gevrnd, gevfit, gevlike.


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Compute statistics of the Burr type XII distribution.



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chi2stat


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 -- statistics: [M, V] = chi2stat (DF)

     Compute statistics of the chi-squared distribution.

     ‘[M, V] = chi2stat (DF)’ returns the mean and variance of the chi-squared
     distribution with DF degrees of freedom.

     The size of M (mean) and V (variance) is the same size of the input
     argument.

     Further information about the chi-squared distribution can be found at
     <https://en.wikipedia.org/wiki/Chi-squared_distribution>

     See also: chi2cdf, chi2inv, chi2pdf, chi2rnd.


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Compute statistics of the chi-squared distribution.



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evstat


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 -- statistics: [M, V] = evstat (MU, SIGMA)

     Compute statistics of the extreme value distribution.

     ‘[M, V] = evstat (MU, SIGMA)’ returns the mean and variance of the extreme
     value distribution (also known as the Gumbel or the type I generalized
     extreme value distribution) with location parameter MU and scale parameter
     SIGMA.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     The type 1 extreme value distribution is also known as the Gumbel
     distribution.  This version is suitable for modeling minima.  The mirror
     image of this distribution can be used to model maxima by negating X.  If Y
     has a Weibull distribution, then ‘X = log (Y)’ has the type 1 extreme value
     distribution.

     Further information about the Gumbel distribution can be found at
     <https://en.wikipedia.org/wiki/Gumbel_distribution>

     See also: evcdf, evinv, evpdf, evrnd, evfit, evlike.


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Compute statistics of the extreme value distribution.



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expstat


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 -- statistics: [M, V] = expstat (MU)

     Compute statistics of the exponential distribution.

     ‘[M, V] = expstat (MU)’ returns the mean and variance of the exponential
     distribution with mean parameter MU.

     The size of M (mean) and V (variance) is the same size of the input
     argument.

     A common alternative parameterization of the exponential distribution is to
     use the parameter λ defined as the mean number of events in an interval as
     opposed to the parameter μ, which is the mean wait time for an event to
     occur.  λ and μ are reciprocals, i.e.  μ = 1 / λ.

     Further information about the exponential distribution can be found at
     <https://en.wikipedia.org/wiki/Exponential_distribution>

     See also: expcdf, expinv, exppdf, exprnd, expfit, explike.


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Compute statistics of the exponential distribution.



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fstat


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 -- statistics: [M, V] = fstat (DF1, DF2)

     Compute statistics of the F-distribution.

     ‘[M, V] = fstat (DF1, DF2)’ returns the mean and variance of the
     F-distribution with DF1 and DF2 degrees of freedom.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Further information about the F-distribution can be found at
     <https://en.wikipedia.org/wiki/F-distribution>

     See also: fcdf, finv, fpdf, frnd.


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Compute statistics of the F-distribution.



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gamstat


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 -- statistics: [M, V] = gamstat (A, B)

     Compute statistics of the Gamma distribution.

     ‘[M, V] = gamstat (A, B)’ returns the mean and variance of the Gamma
     distribution with shape parameter A and scale parameter B.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     OCTAVE/MATLAB use the alternative parameterization given by the pair α, β,
     i.e.  shape A and scale B.  In Wikipedia, the two common parameterizations
     use the pairs k, θ, as shape and scale, and α, β, as shape and rate,
     respectively.  The parameter names A and B used here (for MATLAB
     compatibility) correspond to the parameter notation k, θ instead of the α,
     β as reported in Wikipedia.

     Further information about the Gamma distribution can be found at
     <https://en.wikipedia.org/wiki/Gamma_distribution>

     See also: gamcdf, gaminv, gampdf, gamrnd, gamfit, gamlike.


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Compute statistics of the Gamma distribution.



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geostat


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 -- statistics: [M, V] = geostat (PS)

     Compute statistics of the geometric distribution.

     ‘[M, V] = geostat (PS)’ returns the mean and variance of the geometric
     distribution with probability of success parameter PS.

     The size of M (mean) and V (variance) is the same size of the input
     argument.

     Further information about the geometric distribution can be found at
     <https://en.wikipedia.org/wiki/Geometric_distribution>

     See also: geocdf, geoinv, geopdf, geornd, geofit.


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Compute statistics of the geometric distribution.



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gevstat


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 -- statistics: [M, V] = gevstat (K, SIGMA, MU)

     Compute statistics of the generalized extreme value distribution.

     ‘[M, V] = gevstat (K, SIGMA, MU)’ returns the mean and variance of the
     generalized extreme value distribution with shape parameter K, scale
     parameter SIGMA, and location parameter MU.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     The mean of the GEV distribution is not finite when K >= 1, and the
     variance is not finite when K >= 1/2.  The GEV distribution has positive
     density only for values of X such that K * (X - MU) / SIGMA > -1.

     Further information about the generalized extreme value distribution can be
     found at
     <https://en.wikipedia.org/wiki/Generalized_extreme_value_distribution>

     See also: gevcdf, gevinv, gevpdf, gevrnd, gevfit, gevlike.


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Compute statistics of the generalized extreme value distribution.



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gpstat


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 -- statistics: [M, V] = gpstat (K, SIGMA, THETA)

     Compute statistics of the generalized Pareto distribution.

     ‘[M, V] = gpstat (K, SIGMA, THETA)’ returns the mean and variance of the
     generalized Pareto distribution with shape parameter K, scale parameter
     SIGMA, and location parameter THETA.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     When K = 0 and THETA = 0, the generalized Pareto distribution is equivalent
     to the exponential distribution.  When ‘K > 0’ and ‘THETA = SIGMA / K’, the
     generalized Pareto distribution is equivalent to the Pareto distribution.
     The mean of the generalized Pareto distribution is not finite when ‘K >=
     1’, and the variance is not finite when ‘K >= 1/2’.  When ‘K >= 0’, the
     generalized Pareto distribution has positive density for ‘X > THETA’, or,
     when ‘K < 0’, for ‘0 <= (X - THETA) / SIGMA <= -1 / K’.

     Further information about the generalized Pareto distribution can be found
     at <https://en.wikipedia.org/wiki/Generalized_Pareto_distribution>

     See also: gpcdf, gpinv, gppdf, gprnd, gpfit, gplike.


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Compute statistics of the generalized Pareto distribution.



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hnstat


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 -- statistics: [M, V] = hnstat (MU, SIGMA)

     Compute statistics of the half-normal distribution.

     ‘[M, V] = hnstat (MU, SIGMA)’ returns the mean and variance of the
     half-normal distribution with non-centrality (distance) parameter MU and
     scale parameter SIGMA.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Further information about the half-normal distribution can be found at
     <https://en.wikipedia.org/wiki/Half-normal_distribution>

     See also: norminv, norminv, normpdf, normrnd, normfit, normlike.


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Compute statistics of the half-normal distribution.



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hygestat


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 -- statistics: [MN, V] = hygestat (M, K, N)

     Compute statistics of the hypergeometric distribution.

     ‘[MN, V] = hygestat (M, K, N)’ returns the mean and variance of the
     hypergeometric distribution parameters M, K, and N.

        • M is the total size of the population of the hypergeometric
          distribution.  The elements of M must be positive natural numbers.

        • K is the number of marked items of the hypergeometric distribution.
          The elements of K must be natural numbers.

        • N is the size of the drawn sample of the hypergeometric distribution.
          The elements of N must be positive natural numbers.

     The size of MN (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Further information about the hypergeometric distribution can be found at
     <https://en.wikipedia.org/wiki/Hypergeometric_distribution>

     See also: hygecdf, hygeinv, hygepdf, hygernd.


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Compute statistics of the hypergeometric distribution.



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invgstat


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 -- statistics: [M, V] = invgstat (MU, LAMBDA)

     Compute statistics of the inverse Gaussian distribution.

     ‘[M, V] = invgstat (MU, LAMBDA)’ returns the mean and variance of the
     inverse Gaussian distribution with mean parameter MU and shape parameter
     LAMBDA.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Further information about the inverse Gaussian distribution can be found at
     <https://en.wikipedia.org/wiki/Inverse_Gaussian_distribution>

     See also: invgcdf, invginv, invgpdf, invgrnd, invgfit, invglike.


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Compute statistics of the inverse Gaussian distribution.



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logistat


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 -- statistics: [M, V] = logistat (MU, SIGMA)

     Compute statistics of the logistic distribution.

     ‘[M, V] = logistat (MU, SIGMA)’ returns the mean and variance of the
     logistic distribution with mean parameter MU and scale parameter SIGMA.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Further information about the logistic distribution can be found at
     <https://en.wikipedia.org/wiki/Logistic_distribution>

     See also: logicdf, logiinv, logipdf, logirnd, logifit, logilike.


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Compute statistics of the logistic distribution.



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loglstat


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 -- statistics: [M, V] = loglstat (MU, SIGMA)

     Compute statistics of the loglogistic distribution.

     ‘[M, V] = loglstat (MU, SIGMA)’ returns the mean and variance of the
     loglogistic distribution with mean parameter MU and scale parameter SIGMA.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Further information about the loglogistic distribution can be found at
     <https://en.wikipedia.org/wiki/Log-logistic_distribution>

     OCTAVE/MATLAB use an alternative parameterization given by the pair μ, σ,
     i.e.  MU and SIGMA, in analogy with the logistic distribution.  Their
     relation to the α and b parameters used in Wikipedia are given below:

        • MU = log (A)
        • SIGMA = 1 / A

     See also: logncdf, logninv, lognpdf, lognrnd, lognfit, lognlike.


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Compute statistics of the loglogistic distribution.



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lognstat


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 -- statistics: [M, V] = lognstat (MU, SIGMA)

     Compute statistics of the lognormal distribution.

     ‘[M, V] = lognstat (MU, SIGMA)’ returns the mean and variance of the
     lognormal distribution with mean parameter MU and standard deviation
     parameter SIGMA, each corresponding to the associated normal distribution.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Further information about the lognormal distribution can be found at
     <https://en.wikipedia.org/wiki/Log-normal_distribution>

     See also: logncdf, logninv, lognpdf, lognrnd, lognfit, lognlike.


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Compute statistics of the lognormal distribution.



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nakastat


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 -- statistics: [M, V] = nakastat (MU, OMEGA)

     Compute statistics of the Nakagami distribution.

     ‘[M, V] = nakastat (MU, OMEGA)’ returns the mean and variance of the
     Nakagami distribution with shape parameter MU and spread parameter OMEGA.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Further information about the Nakagami distribution can be found at
     <https://en.wikipedia.org/wiki/Normal_distribution>

     See also: nakacdf, nakainv, nakapdf, nakarnd, nakafit, nakalike.


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Compute statistics of the Nakagami distribution.



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nbinstat


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 -- statistics: [M, V] = nbinstat (R, PS)

     Compute statistics of the negative binomial distribution.

     ‘[M, V] = nbinstat (R, PS)’ returns the mean and variance of the negative
     binomial distribution with parameters R and PS, where R is the number of
     successes until the experiment is stopped and PS is the probability of
     success in each experiment, given the number of failures in X.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Further information about the negative binomial distribution can be found
     at <https://en.wikipedia.org/wiki/Negative_binomial_distribution>

     See also: nbincdf, nbininv, nbininv, nbinrnd, nbinfit, nbinlike.


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Compute statistics of the negative binomial distribution.



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ncfstat


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 -- statistics: [M, V] = ncfstat (DF1, DF1, LAMBDA)

     Compute statistics for the noncentral F-distribution.

     ‘[M, V] = ncfstat (DF1, DF1, LAMBDA)’ returns the mean and variance of the
     noncentral F-distribution with DF1 and DF2 degrees of freedom and
     noncentrality parameter LAMBDA.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Further information about the noncentral F-distribution can be found at
     <https://en.wikipedia.org/wiki/Noncentral_F-distribution>

     See also: ncfcdf, ncfinv, ncfpdf, ncfrnd, fstat.


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Compute statistics for the noncentral F-distribution.



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nctstat


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 -- statistics: [M, V] = nctstat (DF, MU)

     Compute statistics for the noncentral t-distribution.

     ‘[M, V] = nctstat (DF, MU)’ returns the mean and variance of the noncentral
     t-distribution with DF degrees of freedom and noncentrality parameter MU.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Further information about the noncentral t-distribution can be found at
     <https://en.wikipedia.org/wiki/Noncentral_t-distribution>

     See also: nctcdf, nctinv, nctpdf, nctrnd, tstat.


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Compute statistics for the noncentral t-distribution.



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ncx2stat


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 -- statistics: [M, V] = ncx2stat (DF, LAMBDA)

     Compute statistics for the noncentral chi-squared distribution.

     ‘[M, V] = ncx2stat (DF, LAMBDA)’ returns the mean and variance of the
     noncentral chi-squared distribution with DF degrees of freedom and
     noncentrality parameter LAMBDA.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Further information about the noncentral chi-squared distribution can be
     found at
     <https://en.wikipedia.org/wiki/Noncentral_chi-squared_distribution>

     See also: ncx2cdf, ncx2inv, ncx2pdf, ncx2rnd.


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Compute statistics for the noncentral chi-squared distribution.



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normstat


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 -- statistics: [M, V] = normstat (MU, SIGMA)

     Compute statistics of the normal distribution.

     ‘[M, V] = normstat (MU, SIGMA)’ returns the mean and variance of the normal
     distribution with non-centrality (distance) parameter MU and scale
     parameter SIGMA.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Further information about the normal distribution can be found at
     <https://en.wikipedia.org/wiki/Normal_distribution>

     See also: norminv, norminv, normpdf, normrnd, normfit, normlike.


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Compute statistics of the normal distribution.



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plstat


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 -- statistics: [M, V] = plstat (X, FX)

     Compute statistics of the piecewise linear distribution.

     ‘[M, V] = plstat (X, FX)’ returns the mean, M, and variance, V, of the
     piecewise linear distribution with a vector of X values at which the CDF
     changes slope and a vector of CDF values FX that correspond to each value
     in X.  Both X and FX must be vectors of the same size and at least
     2-elements long.

     Further information about the piecewise linear distribution can be found at
     <https://en.wikipedia.org/wiki/Piecewise_linear_function>

     See also: plcdf, plinv, plpdf, plrnd.


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Compute statistics of the piecewise linear distribution.



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poisstat


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 -- statistics: [M, V] = poisstat (LAMBDA)

     Compute statistics of the Poisson distribution.

     ‘[M, V] = poisstat (LAMBDA)’ returns the mean and variance of the Poisson
     distribution with rate parameter LAMBDA.

     The size of M (mean) and V (variance) is the same size of the input
     argument.

     Further information about the Poisson distribution can be found at
     <https://en.wikipedia.org/wiki/Poisson_distribution>

     See also: poisscdf, poissinv, poisspdf, poissrnd, poissfit, poisslike.


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Compute statistics of the Poisson distribution.



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raylstat


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 -- statistics: [M, V] = raylstat (SIGMA)

     Compute statistics of the Rayleigh distribution.

     ‘[M, V] = raylstat (SIGMA)’ returns the mean and variance of the Rayleigh
     distribution with scale parameter SIGMA.

     The size of M (mean) and V (variance) is the same size of the input
     argument.

     Further information about the Rayleigh distribution can be found at
     <https://en.wikipedia.org/wiki/Rayleigh_distribution>

     See also: raylcdf, raylinv, raylpdf, raylrnd, raylfit, rayllike.


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Compute statistics of the Rayleigh distribution.



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ricestat


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 -- statistics: [M, V] = ricestat (S, SIGMA)

     Compute statistics of the Rician distribution.

     ‘[M, V] = ricestat (S, SIGMA)’ returns the mean and variance of the Rician
     distribution with non-centrality (distance) parameter S and scale parameter
     SIGMA.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Further information about the Rician distribution can be found at
     <https://en.wikipedia.org/wiki/Rice_distribution>

     See also: ricecdf, riceinv, ricepdf, ricernd, ricefit, ricelike.


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Compute statistics of the Rician distribution.



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tlsstat


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 -- statistics: [M, V] = tlsstat (MU, SIGMA, NU)

     Compute statistics of the location-scale Student's T distribution.

     ‘[M, V] = tlsstat (MU, SIGMA, NU)’ returns the mean and variance of the
     location-scale Student's T distribution with location parameter MU, scale
     parameter SIGMA, and NU degrees of freedom.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Further information about the location-scale Student's T distribution can
     be found at
     <https://en.wikipedia.org/wiki/Student%27s_t-distribution#Location-scale_t_distribution>

     See also: tlscdf, tlsinv, tlspdf, tlsrnd, tlsfit, tlslike.


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Compute statistics of the location-scale Student's T distribution.



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tristat


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 -- statistics: [M, V] = tristat (A, B, C)

     Compute statistics of the Triangular distribution.

     ‘[M, V] = tristat (A, B, C)’ returns the mean and variance of the
     Triangular distribution with lower limit parameter A, peak location (mode)
     parameter B, and upper limit parameter C.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Note that the order of the parameter input arguments has been changed after
     statistics version 1.6.3 in order to be MATLAB compatible with the
     parameters used in the TriangularDistribution probability distribution
     object.  More specifically, the positions of the parameters B and C have
     been swapped.  As a result, the naming conventions no longer coincide with
     those used in Wikipedia, in which b denotes the upper limit and c denotes
     the mode or peak parameter.

     Further information about the triangular distribution can be found at
     <https://en.wikipedia.org/wiki/Triangular_distribution>

     See also: tcdf, tinv, tpdf, trnd.


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Compute statistics of the Triangular distribution.



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tstat


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 -- statistics: [M, V] = tstat (DF)

     Compute statistics of the Student's T distribution.

     ‘[M, V] = tstat (DF)’ returns the mean and variance of the Student's T
     distribution with DF degrees of freedom.

     The size of M (mean) and V (variance) is the same size of the input
     argument.

     Further information about the Student's T distribution can be found at
     <https://en.wikipedia.org/wiki/Student%27s_t-distribution>

     See also: tcdf, tinv, tpdf, trnd.


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Compute statistics of the Student's T distribution.



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unidstat


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 -- statistics: [M, V] = unidstat (DF)

     Compute statistics of the discrete uniform cumulative distribution.

     ‘[M, V] = unidstat (DF)’ returns the mean and variance of the discrete
     uniform cumulative distribution with parameter N, which corresponds to the
     maximum observable value and must be a positive natural number.

     The size of M (mean) and V (variance) is the same size of the input
     argument.

     Further information about the discrete uniform distribution can be found at
     <https://en.wikipedia.org/wiki/Discrete_uniform_distribution>

     See also: unidcdf, unidinv, unidpdf, unidrnd, unidfit.


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Compute statistics of the discrete uniform cumulative distribution.



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unifstat


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 -- statistics: [M, V] = unifstat (DF)

     Compute statistics of the continuous uniform cumulative distribution.

     ‘[M, V] = unifstat (DF)’ returns the mean and variance of the continuous
     uniform cumulative distribution with parameters A and B, which define the
     lower and upper bounds of the interval [A, B].

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Further information about the continuous uniform distribution can be found
     at <https://en.wikipedia.org/wiki/Continuous_uniform_distribution>

     See also: unifcdf, unifinv, unifpdf, unifrnd, unifit.


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Compute statistics of the continuous uniform cumulative distribution.



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wblstat


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 -- statistics: [M, V] = wblstat (LAMBDA, K)

     Compute statistics of the Weibull distribution.

     ‘[M, V] = wblstat (LAMBDA, K)’ returns the mean and variance of the Weibull
     distribution with scale parameter LAMBDA and shape parameter K.

     The size of M (mean) and V (variance) is the common size of the input
     arguments.  A scalar input functions as a constant matrix of the same size
     as the other inputs.

     Further information about the Weibull distribution can be found at
     <https://en.wikipedia.org/wiki/Weibull_distribution>

     See also: wblcdf, wblinv, wblpdf, wblrnd, wblfit, wbllike, wblplot.


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Compute statistics of the Weibull distribution.





