Abstract
Analysis of covariance (ANCOVA) is a common statistical model. An implicit assumption of ANCOVA is that the covariate is measured without error. However, in many applications, there is covariate measurement error. In this case, the estimates produced by classic ANCOVA methods can include bias, causing predictions and inferences to be inaccurate. This thesis uses monte carlo simulation to examine the effectiveness of an alternative model in estimating the parameters associated with ANCOVA. This model is shown to be effective in accounting for covariate measurement error in the case where there aretwo treatment groups.