I hope that someday Octave will include more statistics functions. If you would like to help improve Octave in this area, please contact @email{bug-octave@bevo.che.wisc.edu}.

__Function File:__**mean***(*`x`)-
If
`x`is a vector, compute the mean of the elements of`x`mean (x) = SUM_i x(i) / N

If

`x`is a matrix, compute the mean for each column and return them in a row vector.

__Function File:__**median***(*`x`)-
If
`x`is a vector, compute the median value of the elements of`x`.x(ceil(N/2)), N odd median(x) = (x(N/2) + x((N/2)+1))/2, N even

If

`x`is a matrix, compute the median value for each column and return them in a row vector.

__Function File:__**std***(*`x`)-
If
`x`is a vector, compute the standard deviation of the elements of`x`.std (x) = sqrt (sumsq (x - mean (x)) / (n - 1))

If

`x`is a matrix, compute the standard deviation for each column and return them in a row vector.

__Function File:__**cov***(*`x`,`y`)-
If each row of
`x`and`y`is an observation and each column is a variable, the (`i`,`j`)-th entry of`cov (`

is the covariance between the`x`,`y`)`i`-th variable in`x`and the`j`-th variable in`y`. If called with one argument, compute`cov (`

.`x`,`x`)

__Function File:__**corrcoef***(*`x`,`y`)-
If each row of
`x`and`y`is an observation and each column is a variable, the (`i`,`j`)-th entry of`corrcoef (`

is the correlation between the`x`,`y`)`i`-th variable in`x`and the`j`-th variable in`y`. If called with one argument, compute`corrcoef (`

.`x`,`x`)

__Function File:__**kurtosis***(*`x`)-
If
`x`is a vector of length`N`, return the kurtosiskurtosis (x) = N^(-1) std(x)^(-4) sum ((x - mean(x)).^4) - 3

of

`x`. If`x`is a matrix, return the row vector containing the kurtosis of each column.

__Function File:__**mahalanobis***(*`x`,`y`)-
Return the Mahalanobis' D-square distance between the multivariate
samples
`x`and`y`, which must have the same number of components (columns), but may have a different number of observations (rows).

__Function File:__**skewness***(*`x`)-
If
`x`is a vector of length`N`, return the skewnessskewness (x) = N^(-1) std(x)^(-3) sum ((x - mean(x)).^3)

of

`x`. If`x`is a matrix, return the row vector containing the skewness of each column.

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