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

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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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

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