# Mr. Bill Doors Mr. Bill Doors

Mr. Bill Doors Mr. Bill Doors is trying to determine the forecasts for the first quarter of 2019 for his best-selling accounting software products, QuickSheets and QuickTax. he first uses seasonalized time series analysis to forecast the total sales of the two products (in ‘000 dollars). Then, separately, he uses exponential smoothing (with a 0.80) on historical data to determine what fraction of total sales represented each product to predict the fraction for the future. Multiplying the forecast for total sales with that for the fraction of total sales represented by a specific product gives him the forecast for each product. The historical data for total sales is shown in Table 1 below Table 1 Quarter 2 3 4 2016 1008 968 818 1288 2017 1162 1046 904 1332 2018 1172 1086 908 1370 A linear regression analysis suggests that the following line best fit the corresponding deseasonalized data series Di 1004 13 *t, where, t-the time period (for the given data, the time periods go from 1 through 12 for the twelve quarters), and D-deseasonalized total sales in period t.

(a) What is the forecast for the total sales of the two products for the first quarter of 2019?

(b) What is the forecast for QuickSheets for the first quarter of 2019 in ‘000 dollars?

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# Mr. Bill Doors Mr. Bill Doors

Mr. Bill Doors Mr. Bill Doors is trying to determine the forecasts for the first quarter of 2019 for his best-selling accounting software products, QuickSheets and QuickTax. he first uses seasonalized time series analysis to forecast the total sales of the two products (in ‘000 dollars). Then, separately, he uses exponential smoothing (with a 0.80) on historical data to determine what fraction of total sales represented each product to predict the fraction for the future. Multiplying the forecast for total sales with that for the fraction of total sales represented by a specific product gives him the forecast for each product. The historical data for total sales is shown in Table 1 below Table 1 Quarter 2 3 4 2016 1008 968 818 1288 2017 1162 1046 904 1332 2018 1172 1086 908 1370 A linear regression analysis suggests that the following line best fit the corresponding deseasonalized data series Di 1004 13 *t, where, t-the time period (for the given data, the time periods go from 1 through 12 for the twelve quarters), and D-deseasonalized total sales in period t.

### Place this order or similar order and get an amazing discount. USE Discount code “GET20” for 20% discount

Posted in Uncategorized