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b. Use the following dummy variables to develop an estimated regression equation to account for any seasonal effects in the data: Qtr1=1 if Quarter 1, 0 otherwise; Qtr2 = 1 if Quarter 2, 0 otherwise; Qtr3= 1 if Quarter 3, 0 otherwise. Round your answers (in thousands of dollars) to whole number. Revenue = Qtrl + Qtr2 + Qtr3 Based only on the seasonal effects in the data, compute estimates of quarterly sales for year 6. Round your answers to whole number. Quarter 1 forecast $ Quarter 2 forecast $ Quarter 3 forecast $ Quarter 4 forecast c. Let Period 1 to refer to the observation in quarter 1 of year 1; Period=2 to refer to the observation in quarter 2 of year 1;... and Period=20 to refer to the observation in quarter 4 of year 3. Using the dummy variables defined in part (b) and Period, develop an estimated regression equation to account for seasonal effects and any linear trend in the time series. Round your answers (in thousands of dollars) to whole number. Enter negative value as negative number. The regression equation is: thousands thousands thousands thousands Revenue Qtrl + Qtr2+ Qtr3 + Period Based upon the seasonal effects in the data and linear trend, compute estimates of quarterly sales for year 6. Round your answers to whole number. Quarter 1 forecast $ Quarter 2 forecast $ Quarter 3 forecast $ Quarter 4 forecast $ thousands thousands thousands thousands

b. Use the following dummy variables to develop an estimated regression equation to account for any seasonal effects in the data: Qtr1=1 if Quarter 1, 0 otherwise; Qtr2 = 1 if Quarter 2, 0 otherwise; Qtr3= 1 if Quarter 3, 0 otherwise. Round your answers (in thousands of dollars) to whole number. Revenue = Qtrl + Qtr2 + Qtr3 Based only on the seasonal effects in the data, compute estimates of quarterly sales for year 6. Round your answers to whole number. Quarter 1 forecast $ Quarter 2 forecast $ Quarter 3 forecast $ Quarter 4 forecast c. Let Period 1 to refer to the observation in quarter 1 of year 1; Period=2 to refer to the observation in quarter 2 of year 1;... and Period=20 to refer to the observation in quarter 4 of year 3. Using the dummy variables defined in part (b) and Period, develop an estimated regression equation to account for seasonal effects and any linear trend in the time series. Round your answers (in thousands of dollars) to whole number. Enter negative value as negative number. The regression equation is: thousands thousands thousands thousands Revenue Qtrl + Qtr2+ Qtr3 + Period Based upon the seasonal effects in the data and linear trend, compute estimates of quarterly sales for year 6. Round your answers to whole number. Quarter 1 forecast $ Quarter 2 forecast $ Quarter 3 forecast $ Quarter 4 forecast $ thousands thousands thousands thousands

Calculus For The Life Sciences
Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter1: Functions
Section1.CR: Chapter 1 Review
Problem 86CR
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South Shore Construction builds permanent docks and seawalls along the southern shore of Long Island, New York. Although the firm has been in business only five years, revenue has increased from $304,000 in the first year of operation to $1,094,000 in the most recent year. The following data show the quarterly sales revenue in thousands of dollars.

Quarter    Year 1    Year 2    Year 3    Year 4    Year 5
    1             25           28          68          94         179    
    2             90          138        151        199        274    
    3            176         255        324        385        451    
    4             13           18          42          74         190    

There appears to be a seasonal pattern in the data and perhaps a [upward linear trend b. Use the following dummy variables to develop an estimated regression equation to account for any seasonal effects in the data: Qtr1 = 1 if Quarter 1, 0 otherwise; Qtr2 = 1 if Quarter 2, 0 otherwise; Qtr3 = 1 if Quarter 3, 0 otherwise. Round your answers (in thousands of dollars) to whole number. Revenue = + Qtrl + Qtr2 + Qtr3 Based only on the seasonal effects in the data, compute estimates of quarterly sales for year 6. Round your answers to whole number. Quarter 1 forecast $ Quarter 2 forecast $ Quarter 3 forecast $ Quarter 4 forecast $ c. Let Period = 1 to refer to the observation in quarter 1 of year 1; Period=2 to refer to the observation in quarter 2 of year 1; ... and Period = 20 to refer to the observation in quarter 4 of year 3. Using the dummy variables defined in part (b) and Period, develop an estimated regression equation to account for seasonal effects and any linear trend in the time series. Round your answers (in thousands of dollars) to whole number. Enter negative value as negative number. The regression equation is: thousands thousands thousands thousands Revenue = + Qtrl + Qtr2 + Qtr3 + Period Based upon the seasonal effects in the data and linear trend, compute estimates of quarterly sales for year 6. Round your answers to whole number. Quarter 1 forecast $ Quarter 2 forecast $ Quarter 3 forecast $ Quarter 4 forecast $ thousands thousands thousands thousands
Transcribed Image Text:There appears to be a seasonal pattern in the data and perhaps a [upward linear trend b. Use the following dummy variables to develop an estimated regression equation to account for any seasonal effects in the data: Qtr1 = 1 if Quarter 1, 0 otherwise; Qtr2 = 1 if Quarter 2, 0 otherwise; Qtr3 = 1 if Quarter 3, 0 otherwise. Round your answers (in thousands of dollars) to whole number. Revenue = + Qtrl + Qtr2 + Qtr3 Based only on the seasonal effects in the data, compute estimates of quarterly sales for year 6. Round your answers to whole number. Quarter 1 forecast $ Quarter 2 forecast $ Quarter 3 forecast $ Quarter 4 forecast $ c. Let Period = 1 to refer to the observation in quarter 1 of year 1; Period=2 to refer to the observation in quarter 2 of year 1; ... and Period = 20 to refer to the observation in quarter 4 of year 3. Using the dummy variables defined in part (b) and Period, develop an estimated regression equation to account for seasonal effects and any linear trend in the time series. Round your answers (in thousands of dollars) to whole number. Enter negative value as negative number. The regression equation is: thousands thousands thousands thousands Revenue = + Qtrl + Qtr2 + Qtr3 + Period Based upon the seasonal effects in the data and linear trend, compute estimates of quarterly sales for year 6. Round your answers to whole number. Quarter 1 forecast $ Quarter 2 forecast $ Quarter 3 forecast $ Quarter 4 forecast $ thousands thousands thousands thousands
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