In this chapter, we explored methods for comparing a quantitative response across \(J\) groups (\(J \ge 2\)), with what is called the One-Way ANOVA procedure. The initial test is based on assessing evidence against a null hypothesis of no difference in the true means for the \(J\) groups. There are two different methods for estimating these One-Way ANOVA models: the cell-means model and the reference-coded versions of the model. There are times when either model will be preferred, but for the rest of the text, the reference coding is used (sorry!). The ANOVA \(F\)-statistic, often presented with underlying information in the ANOVA table, provides a method of assessing evidence against the null hypothesis either using permutations or via the \(F\)-distribution. Pair-wise comparisons using Tukey’s HSD provide a method for comparing all the groups and are a nice complement to the overall ANOVA results. A compact letter display was shown that enhanced the interpretation of Tukey’s HSD result.
In the guinea pig example, we are left with some lingering questions based on these results. It appears that the effect of dosage changes as a function of the delivery method (OJ, VC) because the size of the differences between OJ and VC change for different dosages. These methods can’t directly assess the question of whether the effect of delivery method is the same or not across the different dosages. In Chapter ??, the two variables, Dosage and Delivery method are modeled as two separate variables so we can consider their effects both separately and together. This allows more refined hypotheses, such as Is the effect of delivery method the same for all dosages?, to be tested. This will introduce new models and methods for analyzing data where there are two factors as explanatory variables in a model for a quantitative response variable in what is called the Two-Way ANOVA.