Testing for Heteroscedasticity and/or Autocorrelation in Longitudinal Mixed Effect Nonlinear Models with AR(1) Errors

The effect of influental observation son the parameter estimates of ordinary least squares regression models has received considerable a t t e n t i o n fn the last decade. However, very little attention has been given to the problem of influential observation sinthea naysis of variace . The purpose of this paper is to show by way of examples that in fluential observations can alter the conclusions of tests of hypotheses in the analysis of variance . Regression diagno stics for identifying both extreme points and out liers can be used toreveal potential data and design problems.