Exercise

# Plotting and interpreting results

In the previous exercise, you fit a model to the school data and examined the regression coefficients using `summary()`

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Plotting the regression coefficients and their 95% confidence intervals can be another method to visualize and interpret them.

A positive coefficient indicates that as a coefficient increases, the expected response increases as well.
Likewise, a negative coefficient indicates that as a coefficient increases, the expected response decreases.
If the 95% CI does not include zero, the coefficient can be considered statistically significant.

During this exercise, you will plot the model results using ggplot2.
The model you fit has been loaded for you as `lmerModel`

.

After examining the plot, you will be asked about the results.

Instructions 1/2

**undefined XP**

- Extract out the regression coefficients of
`lmerModel`

using`tidy()`

. - Grab the fixed-effect coefficients of interest.
- Plot the coefficients with
`ggplot()`

.