r, Multiple R, r², R², R Square, R²Adjusted

Abstract

This chapter discusses some terms that are used in correlation analysis and linear regression. r is the correlation coefficient. It is also known as the “Pearson product-moment correlation coefficient”, “PPMCC” or “PCC”, or “Pearson's r”. Multiple R is the “multiple correlation coefficient”. It is a measure of the goodness of fit of the regression model. The “Error” in sum of squares error is the error in the regression line as a model for explaining the data. The purpose of regression analysis is to develop a cause and effect “model” in the form of an equation. There are a number of methods for calculating a line which best fits the data. The one most commonly used is the least squares method. Residuals represent the error in the regression model, the variation of the outcome variable y which is unexplained by the model.