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[project @ 1996-07-19 02:20:16 by jwe]
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author | jwe |
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date | Fri, 19 Jul 1996 02:26:23 +0000 |
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@c Copyright (C) 1996 John W. Eaton @c This is part of the Octave manual. @c For copying conditions, see the file gpl.texi. @node Statistics, Plotting, Sets, Top @chapter Statistics I hope that someday Octave will include more statistics functions. If you would like to help improve Octave in this area, please contact @code{bug-octave@@bevo.che.wisc.edu}. @ftable @code @item corrcoef (@var{x} [, @var{y}]) If each row of @var{x} and @var{y} is an observation and each column is a variable, the (@var{i},@var{j})-th entry of @code{corrcoef (@var{x}, @var{y})} is the correlation between the @var{i}-th variable in @var{x} and the @var{j}-th variable in @var{y}. If invoked with one argument, compute @code{corrcoef (@var{x}, @var{x})}. @item cov (@var{x} [, @var{y}]) If each row of @var{x} and @var{y} is an observation and each column is a variable, the (@var{i},@var{j})-th entry of @code{cov (@var{x}, @var{y})} is the covariance between the @var{i}-th variable in @var{x} and the @var{j}-th variable in @var{y}. If invoked with one argument, compute @code{cov (@var{x}, @var{x})}. @item kurtosis (@var{x}) If @var{x} is a vector of length @var{N}, return the kurtosis @example kurtosis(x) = N^(-1) std(x)^(-4) SUM_i (x(i)-mean(x))^4 - 3 @end example @noindent of @var{x}. If @var{x} is a matrix, return the row vector containing the kurtosis of each column. @item mahalanobis (@var{x}, @var{y}) Returns Mahalanobis' D-square distance between the multivariate samples @var{x} and @var{y}, which must have the same number of components (columns), but may have a different number of observations (rows). @item mean (@var{a}) If @var{a} is a vector, compute the mean of the elements of @var{a}. If @var{a} is a matrix, compute the mean for each column and return them in a row vector. @item median (@var{a}) If @var{a} is a vector, compute the median value of the elements of @var{a}. If @var{a} is a matrix, compute the median value for each column and return them in a row vector. @item skewness (@var{x}) If @var{x} is a vector of length @var{N}, return the skewness @example skewness (x) = N^(-1) std(x)^(-3) SUM_i (x(i)-mean(x))^3 @end example @noindent of @var{x}. If @var{x} is a matrix, return the row vector containing the skewness of each column. @item std (@var{a}) If @var{a} is a vector, compute the standard deviation of the elements of @var{a}. If @var{a} is a matrix, compute the standard deviation for each column and return them in a row vector. @end ftable