Warm up case study: College survey data

  1. Calculate the mean number of colleges students are applying to.
  2. Calculate the standard deviation of the number of schools students are applying to.
  3. Calculate the 5-number summary of the colleges applied to variable and then draw a boxplot by hand showing the distribution of the data.

Case study 1: Comparing male and female heights

The body dimensions dataset id located in google classroom and shows various body measurements for a sample of people. We are interested in comparing the distributions of male and female heights.

  1. Using the Filter function create a two new data sets in google sheets: one for males, and one for females.
  2. Plot the distribution of male and female heights separately, using a histogram. After examining the distributions, does it seem that visually their centers differ? Estimate the mean height for both males and females using the histograms you created.
  3. Use the AVERAGE() function to calculate the mean height for both males and females. What is the difference in mean height between males and females?
  4. Use the STDEV() function to calculate the standard deviation of height for both males and females.
  5. Based on the location of the mean for both males and females, as well as their respective standard deviations, does it seem likely that the mean height for males is greater than or less than the mean height for females? Explain using the empirical rule.

Case study 2: Differences in salaries between positions in MLB

The MLB data is posted in google sheets and displays salary data for all players in the MLB for the 2008 season.

  1. Using the Filter function create a two new data sets in google sheets: one for pitchers, and one for catchers.
  2. Plot the distribution of pitcher and catchers salaries separately, using a histogram. After examining the distributions, does it seem that visually their centers differ? Estimate the mean salary for both pitchers and catchers using the histograms you created.
  3. Use the AVERAGE() function to calculate the mean salary for both pitchers and catchers. What is the difference in mean salary between pitchers and catchers?
  4. Using the PERCENTILE() function calculate the five number summary for both pitchers and catchers.
  5. What is interesting about the 0th percentile for both pitchers and catchers?
  6. Compare the median salary for both pitchers and catchers, then compute the interquartile range for each. When comparing these ranges, does it seem that there is evidence that one position has a higher salary than the other, in general?