This homework assignment was written in R Studio using RMarkdown. Now that we have learned this tool, you will use it to

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This homework assignment was written in R Studio using
RMarkdown. Now that we have learned this tool, you will use it to
type up your answers (including R code). You can use this
document as a template for your own submission (which must include
answers and code in an .Rmd input file and in an output
file). Remember that in order to create your Word document, click
the arrow next to “Knit” in the RStudio toolbar and
choose “Knit to Word.” If you choose to create .html output
instead, you will need to convert this to .pdf before submitting to
Blackboard. I am sharing the .pdf output for this homework, but
also the .Rmd file used to produce it.
Part I - The Mark of a Criminal Record Revisited
In Homework 2, we analyzed data from an important field
experiment by Devah Pager about the the effect of race and criminal
record on employment:
“The Mark of a Criminal Record”. American Journal of
Sociology 108(5):937-975. Look here to watch Professor Pager
discuss the design and result.
This is a follow-up exercise using the same data set. Last time
you encountered the paper, you described the different callback
rates between groups. Now we are going to use what we’ve learned
about statistical inference to better understand those patterns.
You are welcome—and even encouraged—to reuse code from HW2. In
fact, in practice you often have to work with the same dataset many
times, and writing good code the first time helps you reuse the
code in future projects.
The dataset is called criminalrecord.csv. You should be
able to find these data on your computer from the beginning of the
semester. You may not need to use all of these variables for this
activity. We’ve kept these unnecessary variables in the dataset
because it is common to receive a dataset with much more
information than you need.
Name
Description
Job ID number
1 if tester received a callback, 0 if the tester did
not receive a callback.
1 if the tester is black, 0 if the tester is
white.
1 if the tester has a criminal record, 0 if the
tester does not.
1 if tester interacted with employer during the job
application, 0 if tester doesn’t interact with
employer.
1 is job is located in the city center, 0 if job is
located in the suburbs.
Job’s average distance to downtown.
Question 3 (2 points)
Now consider the callback rate for white applicants with a
criminal record. Construct a 95% confidence interval around this
estimate. Then, construct a 95% confidence interval around your
estimate of the callback rate for black applicants without a
criminal record. Recall that
􏰀 pˆ(1 − pˆ) n
is how we have been estimating the standard errors for
proportions.