For this assignment, we will use a dataset from the British National Child Development Study, which began as a study of children born in Britain during the week of March 3, 1985. Information was gathered when these subjects were 7, 11, 16, 23 and 33 years old. Here are the variables we will use in this assignment:

For this assignment, we will use a dataset from the British National Child Development Study, which began
as a study of children born in Britain during the week of March 3, 1985. Information was gathered when
these subjects were 7, 11, 16, 23 and 33 years old. Here are the variables we will use in this assignment:
Variable name Description gwage33 Hourly wages (in British pounds) at age 33
height33 Height (in inches) measured at age 33
The dataset used in this exercise is available in the assignment page for this problem set on Moodle. The file is called Ch3_Exercise3_Height_and_Wages_UK.RData.
Make sure to load the tidyverse, knitr and broom packages before starting with the rest of
the analysis
a. Estimate a model where height at age 33 explains income at age 33. Interpret β1 and β0.
b. Create a scatter plot of height and income at age 33, with the model created in part 1 overlaid over
the plot. What can you say about the fit of the model from looking at the plot? Do you have any
concerns about outliers?
c. Re-estimate the bivariate OLS model from part 1a.) but exclude observations with wages per hour
more than 400 British pounds and height less than 40 inches. Briefly compare results to earlier results.
What happens to the standard errors?
d. Interpret the t-statistics and p-values for the coefficients from exercise 1c.). Do we accept or reject the
null hypothesis that β1 = 0 for α = 0.01 and a two-sided alternative? Explain your answer.
Exercise 2 (4pts)
What determines how much drivers are fined if they are stopped for speeding? To answer this question,
we’ll investigate traffic stops and citations in Massachusetts using data from Makowsky & Stratmann (2009).
Even though state law sets a formula for tickets based on how fast a person was driving, police officers in
practice often deviate from the formula.
1
The dataset used in this exercise is available in the assignment page for this problem set on Moodle. The
file is called Ch5_Exercise4_Speeding_tickets.RData.
a. Estimate a bivariate OLS model in which ticket amount (Amount) is a function of Age. Is age statistically significant? Is endogeneity possible? (1 pt)
b. Show how to calculate the 95 percent confidence interval for the coefficient on Age (you can confirm
your calculation using the tidy command) (1 pt)
c. Estimate the above model also controlling for miles per hour over the speed limit (MPHover). Explain
what happens to the coefficient on age and why. (2 pts)