Plot points by clicking anywhere on the grid, or plot a set of points by entering a pair of coordinates in the text box and clicking Add Point .
Check the Remove Points box and click on any point to remove it. You can also click‑and‑drag any point to change its location.
When you check the box for Student Guess , a purple line will appear on the grid. Drag the purple dots to approximate a line of best fit visually. An equation of this line will appear to the right. A slope and yintercept can also be entered to change the line of best fit. When you check the box for Show Line of Best Fit , the area leastsquares regression line will be displayed. An equation of this line and the correlation coefficient (
r
) will appear.
The grid can be zoomed in and out as more points are added. Use the + and  magnifying glass to zoom. To see a different portion of the grid, highlight the Move Graph box and use the mouse to drag the graph around. You can reset the original parameters for the graph or use the Zoom to Fit box to have the graph automatically select parameters that will show all your points optimally.
The data below shows the points scored and minutes played by the six "starters" for the Los Angeles Lakers during the 200405 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 Ordered Pair  1819,2689  1195,2746  1115,2903  975,2320  735,1870  577,1830 

Check the Computer 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.