Consider the following time series data. (a) Choose the correct time series plot. (iii) (iv) What type of pattern exists in the data? (b) Use a multiple regression model with dummy varisbles as follows to develop an equation to account for seasonal effects in the dsta: Qtr 1 = 1 if Quarter 1 , o ctherwise; Qir2 = 1 ir Quarter 2,0 otherwise; Qte3 = 1 if Quarter 3, 0 otherwise: If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank (Example: -300), If the constant is " 1 it must be entered in the box. Do not round intermediate calculation. (c) Compute the quarterly forecasts for next year based on the model you developed in part (b): If requiced, round your answers to three docimal places. Do not round intermediate calculstion. (d) Use a muitiple 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 Y Y 2 ar 1,t=2 for Quarter 2 in Year 1 p... t=12 for Quarter 4 in Yoar 3 . If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the biank (Example: -300). (e) Compute the quarterly forecasts for next year based on the model you developed in part (d). Do not round your interim computations and round your finat answer to three decimal places. (f) Calculate the MSE for the regression models deveioped in parts (b) and (d). If required, round your intermediate calculations and final answer to three decimal places. Is the model you developed in part (b) or the model you developed in part (d) more effective? The model developed in is more effective because it has the MSE