Important

Go to the course GitHub organization and locate the repo titled `ae-8-rail-trail-YOUR_GITHUB_USERNAME` to get started.

## Packages and data

``````library(tidyverse)
library(tidymodels)

## Exercise 1

Fit a model predicting `volume` from `hightemp` and `season`.

``````rt_mlr_main_fit <- linear_reg() %>%
set_engine("lm") %>%
fit(volume ~ hightemp + season, data = rail_trail)

tidy(rt_mlr_main_fit)``````
``````# A tibble: 4 × 5
term         estimate std.error statistic       p.value
<chr>           <dbl>     <dbl>     <dbl>         <dbl>
1 (Intercept)   -125.       71.7     -1.75  0.0841
2 hightemp         7.54      1.17     6.43  0.00000000692
3 seasonSpring     5.13     34.3      0.150 0.881
4 seasonSummer   -76.8      47.7     -1.61  0.111        ``````

Recreate the following visualization which displays the three regression lines we can draw based on the results of this model.

``# add code here``

## Exercise 2

Add an interaction effect between `hightemp` and `season` and comment on the significance of the interaction predictors. Time permitting, visualize the interaction model as well.

``# add code here``

## Exercise 3

Fit a model predicting `volume` from all available predictors.

``````rt_full_fit <- linear_reg() %>%
set_engine("lm") %>%
fit(volume ~ ., data = rail_trail)

tidy(rt_full_fit)``````
``````# A tibble: 8 × 5
term            estimate std.error statistic p.value
<chr>              <dbl>     <dbl>     <dbl>   <dbl>
1 (Intercept)        17.6      76.6      0.230 0.819
2 hightemp            7.07      2.42     2.92  0.00450
3 avgtemp            -2.04      3.14    -0.648 0.519
4 seasonSpring       35.9      33.0      1.09  0.280
5 seasonSummer       24.2      52.8      0.457 0.649
6 cloudcover         -7.25      3.84    -1.89  0.0627
7 precip            -95.7      42.6     -2.25  0.0273
8 day_typeWeekend    35.9      22.4      1.60  0.113  ``````

Recreate the following visualization which displays a histogram of residuals (y-axis should be frequency) and a density curve overlaid.