ds_list_variance
In probability theory and statistics, variance measures how far a set of numbers is spread out. A variance of zero indicates that all the values are identical. Variance is always non-negative: a small variance indicates that the data points tend to be very close to the mean (expected value) and hence to each other, while a high variance indicates that the data points are very spread out around the mean and from each other.
Population Variance
In general, the population variance of a finite population of size \(N\) with values \(x_i\) is given by
\( \sigma^2 = \frac 1N \sum_{i=1}^N \left(x_i - \mu \right)^2 \)
where
\( \mu = \frac 1N \sum_{i=1}^N x_i \)
is the population mean.
- ds_list_variance(list, sample)
- Returns the variance of the values in a given list.
COPY/// @func ds_list_variance(list, sample)
///
/// @desc Returns the variance of the values in a given list.
/// Computes for a sample or entire population (default).
///
/// @param {list} list list data structure
/// @param {bool} sample true for sample, false for population
///
/// @return {real} variance of values
///
/// GMLscripts.com/license
function ds_list_variance(list, sample=false)
{
var n = ds_list_size(list);
if (n == 0) return undefined;
var avg = 0;
var sum = 0;
for (var i=0; i<n; i++) avg += ds_list_find_value(list, i);
avg /= n;
for (var i=0; i<n; i++) sum += sqr(ds_list_find_value(list, i) - avg);
return sum / (n - real(sample));
}
Contributors: Quimp
GitHub: View · Commits · Blame · Raw