For Part 1 of the project, choose two different team statistics that you suspect might be correlated. For example, you might suspect that the total number of penalty yards charged against an NFL team's defenseis correlated with the number points given up by that defense. Or that the number of walks given up by a baseball team's pitchers is correlated with the team's overall earned run average. Be creative. Your results may show they are not well correlated, and that's OK. Get the two columns of data into Stat Crunch. Compute the mean and standard deviation of each column ,as well as the median and IQR. Produce a boxplot for each column, and include it in your report. Comment on the shape of each distribution, noting any outliers .Determine the z-scores of the largest and smallest values in each column.

next, perform a linear regression of the two data sets. The report should include the two columns of data, a scatter plot of the data along with the regression line. State the regression coefficient, and what the coefficient of determination says about the correlation. Comment about the level of significance of the correlation. (Look at the P-value of the slope.)Interpret the slope of the line in the context of the data, and of the y-intercept, if it makes sense to do so, or state why it doesn't. Demonstrate that the point(-x,-y) satisfies the linear equation. Finally, determine the largest residual value.