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

**undefined XP**

- Run an
`anova()`

on`lmer_out`

.