Correlation

Interface

Correlation

The correlation coefficient provides a normalized view of correlation based on covariance:

where

• = variance of a set of data and
• = covariance of a set of data
• ranges from -1 (for negatively correlated variables) through zero (for uncorrelated variables) to +1

(for positively correlated variables).

While if X and Y are independent we have , the latter does not imply the former.

References:

PlanetMath, http:planetmath.org/encyclopedia/Covariance.html

Example 1

#include <codecogs/statistics/moments/correlation.h>
#include <iostream>
int main()
{
  int x[4] = {3 , 7 , 5 , 6 };
  int y[4] = {4 , 3 , 7 , 1 };
  double corr = Stats::Moments::correlation<int>(4, x , y);
  std::cout << "The correlation of x and y is: " << corr << std::endl;
  return 0;
}

Output:

The correlation of x and y is: -0.278132

Parameters

data1

the actual population data given as the second array

Returns

the correlation of the given set of data

Authors

Anca Filibiu (August 2005)

Source Code

Source code is available when you agree to a GP Licence or buy a Commercial Licence.

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