Calculates Pearson's Linear Correlation Coefficient.
PEARSON(x, y)
x |
- |
An input series. |
y |
- |
An input series |
A number, the correlation coefficient.
W1: gsin(100, .01, 4)
W2: gsin(100, .01, 4, pi/3)
pearson(W1, W2)
returns: 0.5
pearson(W1, W1)
returns: 1.0
pearson(W1, W1/2)
returns: 1.0
pearson(W1, -W1)
returns -1.0
pearson(gsin(100, 0.01, 2), gcos(100, 0.01, 2))
returns -1.867950E-016
Pearson’s correlation coefficient for a population is defined as the covariance of two variables divided by the product of their standard deviations:
By substituting the sample estimates of the covariance and standard deviations, the sample correlation coefficient computed by PEARSON becomes:
where the arithmetic mean is defined as:
PEARSON returns the degree of linear correlation between the two input series. The result ranges from -1 to 1.
PEARSON assumes X and Y have the same number of points.
See LINREG2 to fit a line to X and Y values using the method of least squares.