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The median and percentile functions described in this section operate on sorted data. For convenience we use quantiles, measured on a scale of 0 to 1, instead of percentiles (which use a scale of 0 to 100).
This function returns the median value of sorted_data, a dataset
of length n with stride stride. The elements of the array
must be in ascending numerical order. There are no checks to see
whether the data are sorted, so the function gsl_sort
should
always be used first.
When the dataset has an odd number of elements the median is the value of element (n-1)/2. When the dataset has an even number of elements the median is the mean of the two nearest middle values, elements (n-1)/2 and n/2. Since the algorithm for computing the median involves interpolation this function always returns a floating-point number, even for integer data types.
This function returns a quantile value of sorted_data, a double-precision array of length n with stride stride. The elements of the array must be in ascending numerical order. The quantile is determined by the f, a fraction between 0 and 1. For example, to compute the value of the 75th percentile f should have the value 0.75.
There are no checks to see whether the data are sorted, so the function
gsl_sort
should always be used first.
The quantile is found by interpolation, using the formula
quantile = (1 - \delta) x_i + \delta x_{i+1}
where i is floor
((n - 1)f) and \delta is
(n-1)f - i.
Thus the minimum value of the array (data[0*stride]
) is given by
f equal to zero, the maximum value (data[(n-1)*stride]
) is
given by f equal to one and the median value is given by f
equal to 0.5. Since the algorithm for computing quantiles involves
interpolation this function always returns a floating-point number, even
for integer data types.
Next: Example statistical programs, Previous: Maximum and Minimum values, Up: Statistics [Index]