4.1 — Multivariate OLS Estimators — R Practice

Required Packages & Data

Load all the required packages we will use (note I have installed them already into the cloud project) by running (clicking the green play button) the chunk below:

library(tidyverse) # your friend and mine
library(broom) # for tidy regression
library(modelsummary) # for nice regression tables
library(car) # for vif command

Download and read in (read_csv) the data below.

• speeding_tickets.csv

# run or edit this chunk (if you want to rename the data)

# read in data from url 
# or you could download and upload it to this project instead
speed <- read_csv("https://metricsf22.classes.ryansafner.com/files/data/speeding_tickets.csv")

Rows: 68357 Columns: 9
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (9): Black, Hispanic, Female, Amount, MPHover, Age, OutTown, OutState, S...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

This data comes from a paper by Makowsky and Strattman (2009) that we will examine later. Even though state law sets a formula for tickets based on how fast a person was driving, police officers in practice often deviate from that formula. This dataset includes information on all traffic stops. An amount for the fine is given only for observations in which the police officer decided to assess a fine. There are a number of variables in this dataset, but the one’s we’ll look at are:

Variable	Description
Amount	Amount of fine (in dollars) assessed for speeding
Age	Age of speeding driver (in years)
MPHover	Miles per hour over the speed limit

We want to explore who gets fines, and how much. We’ll come back to the other variables (which are categorical) in this dataset in later lessons.

Question 1

How does the age of a driver affect the amount of the fine? Make a scatterplot of the Amount of the fine (y) and the driver’s Age (x) along with a regression line.

# type your code below in this chunk
ggplot(data = speed)+
  aes(x = Age,
      y = Amount)+
  geom_point()+
  geom_smooth(method = "lm")

`geom_smooth()` using formula 'y ~ x'

Warning: Removed 36683 rows containing non-finite values (stat_smooth).

Warning: Removed 36683 rows containing missing values (geom_point).