Scenario: We have extensive flight data in the data table Airline delays 3, which contains data for every North American flight in the month of March 2019. Our goal is to analyze the departure delay times, expressed in minutes.

a. Assume this is an SRS. Have we satisfied the conditions for inference about the population of flights?

b. Before performing further analysis, use the data filter to hide and exclude canceled flights (cancelled = 1). “Departure Delay” refers to the time difference between the scheduled departure and the time when the aircraft’s door is closed and the plane pulls away from the gate. Develop a 95% confidence interval for the mean departure delay DepDelay and interpret the interval.

c. Sometimes there can be an additional delay between departure from the gate and the start of the flight. “Wheels off time” refers to the moment when the aircraft leaves the ground. Develop a 95% confidence interval for the mean time between scheduled departure and wheels-off (WheelsOff). Report and interpret this interval. (HINT: use Match Pairs.)

d. Now use a matched-pairs approach to estimate (with 95% confidence) the mean difference between departure delay and wheels off delay. Explain what your interval tells you.