This article develops confidence interval procedures for functions of simple, partial, and squared multiple correlation coefficients. It is assumed that the observed multivariate data represent a random sample from a distribution that possesses infinite moments, but there is no requirement that the distribution be normal. The coverage error of conventional one-sided large sample intervals decreases at rate 1√n as n increases, where n is an index of sample size. The coverage error of the proposed intervals decreases at rate 1/n as n increases. The results of a simulation study that evaluates the performance of the proposed intervals is reported and the intervals are illustrated on a real data set.
Boik, Robert J. and Haaland, Ben
"Second-Order Accurate Inference on Simple, Partial, and Multiple Correlations,"
Journal of Modern Applied Statistical Methods:
2, Article 2.
Available at: http://digitalcommons.wayne.edu/jmasm/vol5/iss2/2