(b) Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. (Round your numerical values to three decimal places.) x1 = 1 if quarter 1, 0 otherwise; x2 = 1 if quarter 2,0 otherwise; x3 = 1 if quarter 3,0 otherwise 7.667 x 5x2 2x3 --- (c) Compute the quarterly forecasts for the next year based on the model you developed in part (b). (Round your answers to two decimal places.) quarter 1 forecast 6.67 quarter 2 forecast 2.67 quarter 3 forecast 5.67 quarter 4 forecast 7.67 (d) Use a multiple regression model to develop an equation to account for trend and seasonal effects in the data. Use the dummy variables you developed in part (b) to capture seasonal effects and create a variable t such that t = 1 for quarter 1 in year 1, t = 2 for quarter 2 in year 1, ... t = 12 for quarter 4 in year 3. (Round your numerical values to three decimal places.) , = 3.417 +0.219x; -2.188x, - 1.594x.3 +0.406 x (e) Compute the quarterly forecasts for the next year based on the model you developed in part (d). (Round your answers to two decimal places.) quarter 1 forecast 1 9.92 quarter 2 forecast 5.91 quarter 3 forecast 3 7.92 X quarter 4 forecast 10.91 (1) Is the model you developed in part (b) or the model you developed in part (d) more effective? Justify your answer. The model in part (b) appears to be more effective since there is no linear trend visible in the data. The model in part (d) appears to be more effective since it has a lower MSE than the model in part (b). The model in part (d) appears to be more effective since it has more variables than the model in part (b). The model in part (b) appears to be more effective since it has a higher MSE than the model in part (d). The model in part (b) appears to be more effective since it has a lower MSE than the model in part (d)