Statisticians most often use the linear mixed model to analyze Gaussian longitudinal data. The value and familiarity of the R² statistic in the linear univariate model naturally creates great interest in extending it to the linear mixed model. We define and describe how to compute a model R² statistic for the linear mixed model by using only a single model. The proposed R² statistic measures multivariate association between the repeated outcomes and the fixed effects in the linear mixed model. The R² statistic arises as a 1–1 function of an appropriate F statistic for testing all fixed effects (except typically the intercept) in a full model. The statistic compares the full model with a null model with all fixed effects deleted (except typically the intercept) while retaining exactly the same covariance structure. Furthermore, the R² statistic leads immediately to a natural definition of a partial R² statistic. A mixed model in which ethnicity gives a very small p‐value as a longitudinal predictor of blood pressure (BP) compellingly illustrates the value of the statistic. In sharp contrast to the extreme p‐value, a very small R² , a measure of statistical and scientific importance, indicates that ethnicity has an almost negligible association with the repeated BP outcomes for the study. Copyright © 2008 John Wiley & Sons, Ltd.