The hyperbolic arctangent of this value is used to test hypotheses about it, and is known as the Fisher transformation.
The hyperbolic arctangent of this value is used to test hypotheses about it, and is known as the Fisher transformation. For given sets A and B, this value is equal to the covariance of A and B divided by the product of the standard deviations of A and B. The sampling distribution of this value follows an n minus 2 degrees of freedom t-distribution. An alternative to it is Spearman's rho, which is based on ranks. The square of this value is the amount of variation in the (*) dependent variable that can be explained by the independent variable in a least-squares regression. For a perfectly dependent pair of variables, it is equal to 1. For 10 points, identify this value developed by Pearson which measures how well a particular line fits a series of points.
The hyperbolic arctangent of this value is used to test hypotheses about it, and is known as the Fisher transformation.
R [or correlation; accept R-squared; accept Pearson's coefficient before mention]