Question

(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. 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). y? = 2.417 + -1.031 Qtr1 + -3.688 Qtr2 + 0.656 Qtr3 + 0.656 t (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 final answer to three decimal places. Year Quarter Period Ft 4 1 13 9.917 4 2 14 7.917 4 3 15 12.917 4 4 16 12.917 (f) Calculate the MSE for the regression models developed in parts (b) and (d). If required, round your intermediate calculations and final answer to three decimal places. Model developed in part (b) Model developed in part (d) MSE Is the model you developed in part (b) or the model you developed in part (d) more effective? The model developed in part (d) is more effective because it has the smaller MSE.

          (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.
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).
y? = 2.417 + -1.031 Qtr1 + -3.688 Qtr2 + 0.656 Qtr3 + 0.656 t
(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 final answer to three decimal places.
Year Quarter Period Ft
4 1 13 9.917
4 2 14 7.917
4 3 15 12.917
4 4 16 12.917
(f) Calculate the MSE for the regression models developed in parts (b) and (d).
If required, round your intermediate calculations and final answer to three decimal places.
Model developed in part (b) Model developed in part (d)
MSE
Is the model you developed in part (b) or the model you developed in part (d) more effective?
The model developed in part (d) is more effective because it has the smaller MSE.

(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.
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).
y? = 2.417 + -1.031 Qtr1 + -3.688 Qtr2 + 0.656 Qtr3 + 0.656 t
(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 final answer to three decimal places.
Year Quarter Period Ft
4 1 13 9.917
4 2 14 7.917
4 3 15 12.917
4 4 16 12.917
(f) Calculate the MSE for the regression models developed in parts (b) and (d).
If required, round your intermediate calculations and final answer to three decimal places.
Model developed in part (b) Model developed in part (d)
MSE
Is the model you developed in part (b) or the model you developed in part (d) more effective?
The model developed in part (d) is more effective because it has the smaller MSE.

Added by Phillip P.

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