Sta-209 (Spring 2025) Homework #1

Directions:

• Submit your assignment via P-web.
• For this assignment only (Homework #1) you may submit any file format (R Script, Word Doc w/ copy-pasted R code, etc.)
  • Future assignments will require you to use R Markdown, so you are encouraged to try using R Markdown on this assignment
• Homework is as individual assignment. It’s okay to check-in with your classmates and mentors, but it is not okay to pass off their work as your own.

Question #1

For this question you’ll use the “2015 Boston Marathon” data set located at the URL:

https://remiller1450.github.io/data/BostonMarathon2015.csv

These data record the demographics, residence, finish time and place, and splits of everyone who finished the race.

• Part A: Write code the loads this data set into R and stores it in a object named marathon_data.
• Part B: Inspect the data using the Environment panel or basic functions (like head(), print(), dim() and/or View()). Afterwards, use an R comment (or plain text if using R Markdown) to provide a brief written statement that includes: what a case represents in this data set, how many cases are present, and how many variables (columns) are present.
• Part C: Use an R function to find the type of the variable Official.Time. Based upon this type, it possible to use the mean() function to find the average finish time without coercion? Briefly explain.
• Part D: Similar to Part C, find the type of the variable Age. Based upon this type, it possible to use the mean() function to find the average age of cases without coercion? Briefly explain.

Question #2

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.