Reference no: EM133334804

Questions:

1) Always, the first step of data analysis is a practical look at the data for each column and, then, "cleaning up" the data to prepare it for analysis. These data are taken from the university provided rosters. Make sure that are variable values used for each school are the same for the nominal variables. So, take some time to "clean up" your data and recode any values as appropriate across the three teams. Document any changes made to the data values.

2) Coaches often state that having a "young team" is a cause for lower levels of competitive performance. How might you state this in terms of a statistical question? We will evaluate this claim by comparing the "year in school" variable for the three schools. Obtain frequency distributions by year in school and provide relative frequency tables for all of the players by year in school and by school for each of the three schools. Is there any evidence of major differences in the "age" of the players? Which school appears to have the "youngest" team?

3) Generate an appropriate graphical comparison of the frequencies and relative frequencies of the Year in School variable by school