GNU Octave - Statistics

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# Statistics

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 (x, y)` is the covariance between the 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 (x, y)` is the correlation between the 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 kurtosis

      kurtosis (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 skewness

      skewness (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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