Using the average baseball salary from 2000 through 2013 data for Problem 1 and Problem 2...

Using the average baseball salary from 2000 through 2013 data for Problem 1 and Problem 2 (stored in BBSalaries),

a. perform a residual analysis for each model.

b. compute the standard error of the estimate (SYX) for each model.

c. compute the MAD for each model.

d. On the basis of (a) through (c) and the principle of parsimony, which forecasting model would you select? Discuss.

Problem 1: The average salary of Major League Baseball players on opening day from 2000 to 2013 is stored in BBSalaries and shown below.

Source: Data extracted from “Baseball Salaries,” USA Today, April 6, 2009, p. 6C; and mlb.com.

a. Plot the data.

b. Compute a linear trend forecasting equation and plot the trend line.

c. Compute a quadratic trend forecasting equation and plot the results.

d. Compute an exponential trend forecasting equation and plot the results.

e. Which model is the most appropriate?

f. Using the most appropriate model, forecast the average salary for 2014.

Problem 2: Using the average baseball salary from 2000 through 2013 data for Problem 1 (stored in BBSalaries),

a. fit a third-order autoregressive model to the average baseball salary and test for the significance of the third-order autoregressive parameter. (Use α = 0.05.)

b. if necessary, fit a second-order autoregressive model to the average baseball salary and test for the significance of the second-order autoregressive parameter. (Use α = 0.05.)

c. if necessary, fit a first-order autoregressive model to the average baseball salary and test for the significance of the first-order autoregressive parameter. (Use α = 0.05.)

d. forecast the average baseball salary for 2014.