The data below shows the points scored and minutes played by the six "starters" for the Los Angeles Lakers during the 2004–05 season. (For this investigation, a "starter" is any player who averaged more than 20 minutes per game.)
Plot points scored along the horizontal axis and minutes along the vertical axis.
PLAYER  Points  Minutes  Kobe Bryant  1819  2689  Caron Butler  1195  2746  Chucky Atkins  1115  2903  Lamar Odom  975  2320  Chris Mihm  735  1870  Jumaine Jones  577  1830 
  Enter  1819,2689  1195,2746  1115,2903  975,2320  735,1870  577,1830 

Check the Show Line of Best Fit box to see a linear approximation of this data. The correlation coefficient (r) indicates how well the line approximates the data. If r = 1, the line is a perfect fit to the data; if r = 0, the line does not fit the data at all. In general, the closer r is to 1, the better the fit.
 What is the correlation coefficient (r) for this set of data?
 Remove the data for Kobe Bryant. How does this change the regression equation and r value?
 Replace the data for Kobe Bryant, and remove the data for another player. Repeat this process for each player in the list. For which player does the removal of data have the greatest impact on the regression equation and r value? What does the change indicate?
 Can you explain the changes that occurred when data was removed?
You can conduct similar investigations for other sports by looking at the statistics for Major League Baseball (MLB), National Football League (NFL), Women's National Basketball Association (WNBA), Major League Soccer (MLS), or other sports that interest you.