The 2017-18 National Baseball Association (NBA) season was the most successful season since the league began in 1946. Attendance set a new record, fans were subscribing to the league's streaming service, and sales of merchandise were up. You want to develop a regression model to predict the number of wins achieved by each NBA team, based on field goal success rate (field goal percentage) and mean attendance (attendance per game). The data are stored in NBA2018.

a. State the multiple regression equation.

b. Interpret the meaning of the slopes in this equation.

c. Predict the mean number of wins for a team that has a field goal percentage of \(40 \%\) and an average attendance of 17,000 .

d. Perform a residual analysis on your model and determine whether the regression assumptions are valid.

e. Is there a significant relationship between the number of wins and the two independent variables (field goal percentage and attendance) at the 0.05 level of significance?

f. Determine the \(p\)-value in (e) and interpret its meaning.
g. Interpret the meaning of the coefficient of multiple determination in this problem.
h. Determine the adjusted \(r^{2}\).
i. At the 0.05 level of significance, determine whether each independent variable makes a significant contribution to the regression model. Indicate the most appropriate regression model for this set of data. j. Determine the \(p\)-values in (i) and interpret their meaning.
k. Compute and interpret the coefficients of partial determination.
l. What conclusions can you reach concerning field goal percentage and attendance in predicting the number of wins?