Sta-209 (Spring 2025) Homework #2

Directions:

• Submit your assignment via P-web.
• Submit only a compiled R Markdown document (pdf, word, or html output are all okay, but you may need to “zip” an html file)
  • If you want to compile to a pdf you can install the tinytext package by running install.packages('tinytex') followed by tinytex::install_tinytex()
• Only submit your .Rmd file if you are unable to compile it due to errors (in the future you will be penalized for this)

Question #1

For this question you’ll use the “NYC Airplanes” data set located at the URL:

https://remiller1450.github.io/data/planes.csv

These data record the characteristics of all airplanes that departed from one of New York City’s major commercial airports (JFK, LGA, or EWR) in the year 2013.

• Part A: Write code the loads this data set into R and stores it in a object named airplanes_data
• Part B: Inspect the data using the Environment panel or basic functions (like head(), print(), dim() and/or View()). Based upon your assessment, how many quantitative variables are recorded? (Hint: the NA you see in the variable speed is how R represents missing data. You should still count this variable as quantitative despite its high prevalence of missing data)
• Part C: Use the table() to create a one-way frequency table of the variable engines (be careful not to use the variable engine). Based upon what you see, do you think it’d be appropriate to treat the variable engines as categorical despite the fact that it records numerical information? Briefly explain.
• Part D: Use the mean() and median() functions to find the mean and median of the variable seats. Noting that standard deviation of seats is 73.7 seats, would you consider the mean and median to be similar to each other (suggesting a roughly symmetric distribution) or different from each other (suggesting a skewed distribution)? Briefly explain.

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Question #2

For this question you’ll use the “Police” data set located at the URL:

https://remiller1450.github.io/data/Police.csv

This data set was assembled by the Washington Post to document all fatal shootings by a police officer from 2015 through mid-2020.

• Part A: Write code that loads this data set into R and stores it as an object named police
• Part B: Create a data visualization showing the distribution of the variable race. Briefly describe the distribution.
• Part C: Use the table() and prop.table() functions to calculate the proportion of cases belonging to each category of the variable race
• Part D: Create an appropriate data visualization showing the relationship between the variables race and body_camera under the hypothesis that an individual’s race is linked to how likely it is that a body camera were present during the incident. Do you believe these variables are associated? Briefly explain.
• Part E: Create a two-way frequency table using the variables race and body_camera. Use this table to find the proportion of White individuals (race = "w") for whom a body camera was present at the time of their shooting. Show how this proportion is calculated.

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Question #3

For this question you’ll use the “Infant Heart” data set located at the URL:

https://remiller1450.github.io/data/InfantHeart.csv

This data set records the results of a randomized experiment at Harvard Medical School to compare two different surgical approaches, “low-flow bypass” and “circulatory arrest”, in infants born with congenital heart defects that require surgery shortly after birth. The study’s main outcomes are the child’s Psychomotor Development Index (PDI), a composite score measuring physiological development, and their Mental Development Index (MDI), a composite score measuring mental development, 2-years after the surgery. For both measures a higher score reflects greater development.

• Part A: Load the data into R as an object named heart, then create a data visualization showing the distribution of the outcome variable MDI
• Part B: Briefly describe the distribution of MDI using your graph from Part A
• Part C: Create a data visualization showing the relationship between the variables Treatment and MDI. Do you believe these variables are associated? Briefly explain.
• Part D: Create a data visualization showing the relationship between the study’s two primary outcomes, MDI and PDI. Add a smoother to your graph to help show the relationship between these variables.
• Part E: Using the graph you created in Part D, describe the relationship between MDI and PDI