Here we'll compare the use of the t-sample t-test t.test() , the one.way test oneway.test() and our old friend lm() for fitting models with categorical predictors.

Here we'll introduce anova() and TukeyHSD() which help us understand our linear model in ways that complement the output from summary()

In this video we'll consider how we could deal with violations of regression assumptions in ANOVA.

In this video we'll consider how we can combine multiple predictors to increase the utility of our models, and we'll consider how to interpret the output when we have many parameters reported.

One of the challenges of using mutliple predictor variables is the reality that there can be interactions between them . In this video we'll consider how to specify models with interactions, how to test interactions for significance, and how to calculate group means when interactions are present (using our old friend predict()).

Interactions can be difficult to understand - a good plot can greatly simplify this as we'll see with the use of interaction.plot() in this video.

We've already introduced TukeyHSD() for testing all pairwise comparisons in our model. He're we'll demonstrate why control of the family-wise error provided by TukeyHSD() is so important - as we'll see adding an interaction greatly increases the number of pairwise comparisons.

We'll close out this chapter by introducing the function HSD.test() from the package "agricolae". This provides a couple of very useful features for working with ANOVA models.