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  3. Hierarchical and Mixed Effects Models in R

Exercise

Comparing regressions and ANOVAs

In the previous exercise, you built a regression model. Two methods for statistical inference include examining the amount of variance explained by coefficients in the model (an ANOVA-like analysis) and using linear predictor variables to model the data (a regression analysis framework). The choice of approaches largely depends upon personal preference and statistical training. Both of these approaches may be done using frequentists or Bayesian methods. Although this course only uses frequentist methods, the same ideas apply to Bayesian models.

The lmer_out model you build in the previous exercise has been loaded for you. First, you will run an anova() on it to see if group explains a significant amount of variability. Second, you will examine the regression coefficient from group to see if it significantly differs from zero.

Instructions 1/3

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  • Run an anova() on lmer_out.