The general linear model was described, and the influence that measurement errors have on model parameters was discussed. In particular, the assumptions of classical true-score theory were used to develop algebraic relationships between the squared multiple correlations coefficient and the regression coefficients in the infallible and fallible models. In addition, a Monte Carlo study was conducted to describe the changes in the infallible parameters due to measurement errors in the criterion variable and the predictor variables. The results indicated that the fallible squared multiple correlation was always less than its infallible counterpart, while the fallible regression coefficients either were greater than or less than their counterparts, depending on the size of their reliability of the criterion relative to the reliabilities of the predictors. The results were discussed within the context of regression analysis, analysis of variance, and analysis of covariance. (Author/GDC)