AE 8: Rail Trail

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)

rail_trail <- read_csv("data/rail_trail.csv")

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.