Recently, fixed mortgage rates have been at historical lows due to the housing slowdown. The data table linked below shows the30-year fixed average mortgage rate for the month of December every year between 1987 and 2010. Use these data to complete parts a through e below.

b. Forecast the average December mortgage rate in 2011 using a trend projection. The forecasted average December mortgage rate in 2011 is 945. (Round to two decimal places as needed.) o. Calculate the MAD for this forecast. The MAD for this forecast is (Round to two decimal places as needed.) d. Check for the presence of positive autooorrelation. Use at = 0.05. Identify the null and alternative hypotheses. Choose the correct answer below. Q A. H0: No positive autooorrelation is present 0 B. Ho: Negative autocorrelation is present H1: Positive autocorrelation is present H1: Positive autocorrelation is present 0 C. H0: No negative autooorrelation is present 0 D. Ho: Positive autocorrelation is present H1: Negative autooorrelation is present H1: No positive autocorrelation is present Determine the DurbinWatson statistic. d = (Round to two decimal places as needed.) Identify the critical values. dL = dU = (Round to two decimal places as needed.) State the conclusion. Choose the correct answer below. 0 A. Fail to reject H0 and conclude that no positive autocorrelation is present. 0 B. Fail to reject Ho and conclude that a positive autooorrelation is present. 0 C. The test is inconclusive. O D. Reject Ho and conclude that no positive autumn-elation is present. 0 E. Reject H3 and conclude that a positive autocorrelation is present. a. What limitations do you foresee with this forecasting method projecting mortgage rates several years into the future? Select all answers that apply. Mortgage rates are eventually going to increase. while the trend projection predicts them to decrease forever. . The trend projection will eventually predict mortgage rates of 0%, or less than 0%, which does not make sense in this context. . Trend projections only forecast accurately for data with strong negative autocorrelation. . The trend projection is linear. While the data appear to be linear over the given time period, it is not safe to assume that they will continue to be. \fi Durbin-Watson Critical Value Table X a = 0.05 k = 1 k = 2 k = 3 d d du d, du 15 1.08 1.36 0.95 1.54 0.82 1.75 16 1.10 1.37 0.98 1.54 0.86 1.73 17 1.13 1.38 1.02 1.54 0.90 1.71 18 1.16 1.39 1.05 1.53 0.93 1.69 19 1.18 1.40 1.08 1.53 0.97 1.68 20 1.20 1.41 1.10 1.54 1.00 1.68 21 1.22 1.42 1.13 1.54 1.03 1.67 22 1.24 1.43 1.15 1.54 1.05 1.66 23 1.26 1.44 1.17 1.54 1.08 1.66 24 1.27 1.45 1.19 1.55 1.10 1.66 25 1.29 1.45 1.21 1.55 1.12 1.66 Print Done