This language is somewhat flexible, just don’t call a specific level a factor. (ANOVAs can be 3-way or more, but these are less common or end up with different names.) A factor is the categorical variable that you are evaluating, and the different categories within the factor are often called levels or groups. I can’t promise that I will cover it all, but it should help to know that ANOVAs are typically referred to as 1-way and 2-way, which is just a way of saying how many factors are being examined in the model.
Like any statistical routine, ANOVA also comes with it’s own set of vocabulary. This tutorial explores both the features and functions of ANOVA as handled by R. Although ANOVA is relatively simple compared to many statistical models, there are still some ins and outs and what-have-yous to consider. While it is commonly used for categorical data, because ANOVA is a type of linear model it can be modified to include continuous data. ANOVA is one of the most basic yet powerful statistical models you have at your disopsal. 10.6 Which model selection should I use?ĪNOVA (or AOV) is short for ANalysis Of VAriance.10.1 Implicit and explicit model selection.9.6 Types of models with random effects.9.4 When are random effects appropriate?.8.3.1 Binomial Linear Regression Example.8.2.1 Poisson linear Regression Example.6.5.1 Post-hoc Means: Multiple Comparisons.6.5 Post-fitting: After You Fit a Model.6.4 Pre-fitting: Before You Fit a Model.3.1 Motivating Data Collection and Management.2.5.2 Properties and Functions of Probability Distributions.2.2.1 Final thoughts before getting into R.
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