In measuring visual acuity, the extremes of a set of normally distributed measures are of concern, together with one or more covariates. This leads to a model in which (X, Y1,Y2) are jointly normally distributed withY1,Y2exchangeable and (X, Yi) having a common correlation. Inferential procedures are developed for correlations and linear regressions amongXand the orderedYvalues. This requires determination of the covariance matrix ofX, Y(1)= min{Y1,Y2} andY(2)= max{Y1,Y2}. The inadequacy of certain estimates that ignore the nonnormality of {X, Y(1),Y(2)} is also discussed. Although the bivariate case is emphasized because of the context of the visual acuity model, many results are given for the more general multivariate case.