Title: Statistics

1 Methods

GSL::Stats.mean(v)
GSL::Vector#mean
Arithmetic mean.
GSL::Stats.variance_m(v[, mean])
GSL::Vector#variance_m([mean])
Variance of v relative to the given value of mean.
GSL::Stats.sd(v[, mean])
GSL::Vector#sd([mean])
Standard deviation.
GSL::Stats.variance_with_fixed_mean(v, mean)
GSL::Vector#variance_with_fixed_mean(mean)
Unbiased estimate of the variance of v when the population mean mean of the underlying distribution is known a priori.
GSL::Stats.variance_with_fixed_mean(v, mean)
GSL::Vector#variance_with_fixed_mean(mean)
GSL::Stats.sd_with_fixed_mean(v, mean)
GSL::Vector#sd_with_fixed_mean(mean)
Unbiased estimate of the variance of v when the population mean mean of the underlying distribution is known a priori.
GSL::Stats.absdev(v[, mean])
GSL::Vector#absdev([mean])
Compute the absolute deviation (from the mean mean if given).
GSL::Stats.skew(v[, mean, sd])
GSL::Vector#skew([mean, sd])
Skewness
GSL::Stats.kurtosis(v[, mean, sd])
GSL::Vector#kurtosis([mean, sd])
Kurtosis
GSL::Stats.lag1_autocorrelation(v[, mean])
GSL::Vector#lag1_autocorrelation([mean])
The lag-1 autocorrelation
GSL::Stats.median_from_sorted_data(v)
GSL::Vector#median_from_sorted_data
Return the median value. The elements of the data must be in ascending numerical order. There are no checks to see whether the data are sorted, so the method GSL::Vector#sort should always be used first.
GSL::Stats.quantile_from_sorted_data(v)
GSL::Vector#quantile_from_sorted_data
Return the quantile value. The elements of the data must be in ascending numerical order. There are no checks to see whether the data are sorted, so the method GSL::Vector#sort should always be used first.
GSL::Stats.covariance(v1, v2)
GSL::Stats.covariance_m(v1, v2, mean1, mean2)
Covariance of vectors v1, v2.

2 Weighted samples

GSL::Vector#wmean(w)
GSL::Vector#wvariance(w)
GSL::Vector#wsd(w)
GSL::Vector#wabsdev(w)
GSL::Vector#wskew(w)
GSL::Vector#wkurtosis(w)

3 Example

#!/usr/bin/env ruby
require 'gsl'

ary =  [17.2, 18.1, 16.5, 18.3, 12.6]
data = Vector.new(ary)
mean     = data.mean()
variance = data.stats_variance()
largest  = data.stats_max()
smallest = data.stats_min()

printf("The dataset is %g, %g, %g, %g, %g\n",
       data[0], data[1], data[2], data[3], data[4]);

printf("The sample mean is %g\n", mean);
printf("The estimated variance is %g\n", variance);
printf("The largest value is %g\n", largest);
printf("The smallest value is %g\n", smallest);

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