Next: Basic Statistical Functions, Up: Statistics

### 25.1 Descriptive Statistics

Octave can compute various statistics such as the moments of a data set.

— Function File: mean (x, dim, opt)

> 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.

> With the optional argument opt, the kind of mean computed can be selected. The following options are recognized:

> `"a"`
>   Compute the (ordinary) arithmetic mean. This is the default.
> `"g"`
>   Computer the geometric mean.
> `"h"`
>   Compute the harmonic mean.

> If the optional argument dim is supplied, work along dimension dim.

> Both dim and opt are optional. If both are supplied, either may appear first.

— Function File: median (x, dim)

> If x is a vector, compute the median value of the elements of x. If the elements of x are sorted, the median is defined as

>                           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. If the optional dim argument is given, operate along this dimension.

> See also: std, mean.

— Function File: meansq (x)
— Function File: meansq (x, dim)

> For vector arguments, return the mean square of the values. For matrix arguments, return a row vector contaning the mean square of each column. With the optional dim argument, returns the mean squared of the values along this dimension.

— Function File: std (x)
— Function File: std (x, opt)
— Function File: std (x, opt, dim)

> 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.

> The argument opt determines the type of normalization to use. Valid values are

> 0:
>   normalizes with N-1, provides the square root of best unbiased estimator of the variance [default]
> 1:
>   normalizes with N, this provides the square root of the second moment around the mean

> The third argument dim determines the dimension along which the standard deviation is calculated.

> See also: mean, median.

— Function File: var (x)

> For vector arguments, return the (real) variance of the values. For matrix arguments, return a row vector contaning the variance for each column.

> The argument opt determines the type of normalization to use. Valid values are

> 0:
>   Normalizes with N-1, provides the best unbiased estimator of the variance [default].
> 1:
>   Normalizes with N, this provides the second moment around the mean.

> The third argument dim determines the dimension along which the variance is calculated.

— Function File: cov (x, y)

> Compute covariance.

> 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: cor (x, y)

> Compute correlation.

> The (i, j)-th entry of `cor (`x`,` y`)` is the correlation between the i-th variable in x and the j-th variable in y.

>               corrcoef(x,y) = cov(x,y)/(std(x)*std(y))

> For matrices, each row is an observation and each column a variable; vectors are always observations and may be row or column vectors.

> `cor (`x`)` is equivalent to `cor (`x`,` x`)`.

> Note that the `corrcoef` function does the same as `cor`.

— Function File: corrcoef (x, y)

> Compute correlation.

> 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.

>               corrcoef(x,y) = cov(x,y)/(std(x)*std(y))

> If called with one argument, compute `corrcoef (`x`,` x`)`.

— Function File: kurtosis (x, dim)

> 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 kurtosis over the first non-singleton dimension. The optional argument dim can be given to force the kurtosis to be given over that dimension.

— Function File: skewness (x, dim)

> 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 skewness along the first non-singleton dimension of the matrix. If the optional dim argument is given, operate along this dimension.

— Function File: statistics (x)

> If x is a matrix, return a matrix with the minimum, first quartile, median, third quartile, maximum, mean, standard deviation, skewness and kurtosis of the columns of x as its rows.

> If x is a vector, treat it as a column vector.

— Function File: moment (x, p, opt, dim)

> If x is a vector, compute the p-th moment of x.

> If x is a matrix, return the row vector containing the p-th moment of each column.

> With the optional string opt, the kind of moment to be computed can be specified. If opt contains `"c"` or `"a"`, central and/or absolute moments are returned. For example,

>               moment (x, 3, "ac")

> computes the third central absolute moment of x.

> If the optional argument dim is supplied, work along dimension dim.