Consider the following time series data. (a) Construct a time series plot. What type of pattem exists in the data? The time series plot shows a horizontal pattem, but there is also a seasonal pattern in the data. The time series plot shows a horizontal pattem and no seasonal pattern in the data. The time series plot shows a linear trend and a seasonal pattern in the data. The time series plot shows a linear trend and no seasonal pattern in the data. (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 nur x1=1 if quarter 1,0 othervise; x2=1 if quarter 2,0 otherwise; x3=1 if quarter 3,0 otherwise y^t= (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 quarter 2 forecast quarter 3 forecast quarter 4 forecast (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 quarter 2 forecast quarter 3 forecast quarter 4 forecast (f) 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 (b) appears to be more effective since it has a higher MSE than the model in part (d). 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 (d) appears to be more effective since it has a lower MSE than the model in part (b). The model in part (b) appears to be more effective since it has a lower MSE than the model in part (d). Consider the following time series data. (a) Construct a time series plot. What type of pattem exists in the data? The time series plot shows a horizontal pattem, but there is also a seasonal pattern in the data. The time series plot shows a horizontal pattem and no seasonal pattern in the data. The time series plot shows a linear trend and a seasonal pattern in the data. The time series plot shows a linear trend and no seasonal pattern in the data. (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 nur x1=1 if quarter 1,0 othervise; x2=1 if quarter 2,0 otherwise; x3=1 if quarter 3,0 otherwise y^t= (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 quarter 2 forecast quarter 3 forecast quarter 4 forecast (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 quarter 2 forecast quarter 3 forecast quarter 4 forecast (f) 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 (b) appears to be more effective since it has a higher MSE than the model in part (d). 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 (d) appears to be more effective since it has a lower MSE than the model in part (b). The model in part (b) appears to be more effective since it has a lower MSE than the model in part (d)