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1. The file P03_54.xlsx lists data for 593 movies released in 2011. Obviously, some movies are simply more popular than others, but success in 2011, measured by 2011 gross or 2011 tickets sold, could also be influenced by the release date. To check this, create a new variable, Days Out, which is the number of days the movie was out during 2011. For example, a movie released on 12/15 would have Days Out equal to 17 (which includes the release day). Create two scatterplots and corresponding correlations, one of 2011 Gross (Y axis) versus Days Out and one of 2011 Tickets Sold (Y axis) versus Days Out. Describe the behavior you see. Do you think a movieâ€™s success can be predicted very well just by knowing how many days it has been out?
2.The file P03_57.xlsx lists the average salary for each NFL team from 2002 to 2009, along with the number of team wins each of these years. Answer the same questions as in problem 55 for this football data.
3. The file P03_58.xlsx lists salaries of MLB players in the years 2007 to 2009. Each row corresponds to a particular player. As indicated by blank salaries, some players played in one of these years, some played in two of these years, and the rest played in all three years. a. Create a new Yes/No variable, All 3 Years, that indicates which players played all three years. b. Create two pivot tables and corresponding pivot charts. The first should show the count of players by position who played all three years. The second should show the average salary each year, by position, for all players who played all three years. (For each of these, put the All 3 Years variable in the Filters area.) Explain briefly what these two pivot tables indicate. c. Define a StatTools data set on only the players who played all three years. Using this data set, create a table of correlations of the three salary variables. What do these correlations indicate about player salaries?