One of the innovative approaches in the use of hierarchical linear models (HLM) is to use HLM for Slopes as Outcomes models. This implies that the researcher considers that the regression slopes vary from cluster to cluster randomly as well as systematically with certain covariates at the cluster level. Among the covariates, group indicator variables at the cluster level, which classify the cluster units into several groups, are often found to be significant predictors. If this is the case, the average relationships between the outcome and a key independent variable are different from group to group. Then the question arises, "At what range of the independent variable is the outcome statistically significantly different between groups?" The Johnson-Neyman (J-N) technique answers this kind of question in analysis of covariance (ANCOVA) settings. In the multi-level modeling context, the F-test, which is used in ANCOVA, cannot be applied because the assumption of homogeneity of variance within cluster units is violated in most cases of data that have multi-level structure. Instead, the approximate Walt test can be used to determine the region of significance. The Mathematica computer software package, which is capable of symbolic processing, leads to a direct solution. Two examples from education and child development are provided in order to illustrate the technique and to show how to implement it with Mathematica. (Contains 10 figures, 6 tables, and 16 references.) (Author/SLD)