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(c) Let = 1 refer to the observation in quarter 1 of year 1; t = 2 refer to the observation in quarter 2 of year 1; seasonal effects and any linear trend in the time series. (Round your numerical values to one decimal place.) and t = 20 refer to the observation in quarter 4 of year 5. Using the dummy variables defined in part (b) and t, develop an estimated regression equation for the time series data (in $1,000s) to acco Based upon the seasonal effects in the data and linear trend, compute estimates of quarterly sales (in $1,000s) for year 6. (Round your answers to the nearest thousand dollars.) quarter 1 forecast thousand

(c) Let = 1 refer to the observation in quarter 1 of year 1; t = 2 refer to the observation in quarter 2 of year 1; seasonal effects and any linear trend in the time series. (Round your numerical values to one decimal place.) and t = 20 refer to the observation in quarter 4 of year 5. Using the dummy variables defined in part (b) and t, develop an estimated regression equation for the time series data (in $1,000s) to acco Based upon the seasonal effects in the data and linear trend, compute estimates of quarterly sales (in $1,000s) for year 6. (Round your answers to the nearest thousand dollars.) quarter 1 forecast thousand

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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(c) Let t = 1 refer to the observation in quarter 1 of year 1; t = 2 refer to the observation in quarter 2 of year 1; seasonal effects and any linear trend in the time series. (Round your numerical values to one decimal place.) and t = 20 refer to the observation in quarter 4 of year 5. Using the dummy variables defined in part (b) and t, develop an estimated regression equation for the time series data (in $1,000s) to account for Based upon the seasonal effects in the data and linear trend, compute estimates of quarterly sales (in $1,000s) for year 6. (Round your answers to the nearest thousand dollars.) quarter 1 forecast $ thousand thousand quarter 2 forecast $ quarter 3 forecast thousand quarter 4 forecast thousand $ $
Transcribed Image Text:(c) Let t = 1 refer to the observation in quarter 1 of year 1; t = 2 refer to the observation in quarter 2 of year 1; seasonal effects and any linear trend in the time series. (Round your numerical values to one decimal place.) and t = 20 refer to the observation in quarter 4 of year 5. Using the dummy variables defined in part (b) and t, develop an estimated regression equation for the time series data (in $1,000s) to account for Based upon the seasonal effects in the data and linear trend, compute estimates of quarterly sales (in $1,000s) for year 6. (Round your answers to the nearest thousand dollars.) quarter 1 forecast $ thousand thousand quarter 2 forecast $ quarter 3 forecast thousand quarter 4 forecast thousand $ $
A construction company 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 $298,000 in the first year of operation to $1,069,000 in the most recent year. The following data show the quarterly sales revenue in thousands of dollars. Quarter O 1 3 4 500- 450 400- 0 350- 300+ 250+ 200- 150- 100- 50 (a) Construct a time series plot. Year 1 15 100 170 1 2 3 4 Year 1 13 quarter 3 forecast quarter 4 forecast Year 2 32 M 136 240 $ $ 26 $ $ Year 3 70 155 321 1 2 3 4 1 2 3 4 Year 2 Year 3 Year/Quarter 48 Year 4 1 2 3 4 Year 4 87 202 379 82 Year 5 1 2 3 4 Year 5 171 282 440 176 500 T 450 400+ What type of pattern exists in the data? O The time series plot shows only a linear trend. O The time series plot shows neither a linear trend nor seasonal effects. O The time series plot shows only seasonal effects. The time series plot shows both a linear trend and seasonal effects. 350+ 300+ 250- 200- 150- 100 50 0- 1 2 3 4 Year 1 Based only on the seasonal effects in the data, compute estimates of quarterly sales (in $1,000s) for year 6. quarter 1 forecast thousand quarter 2 forecast thousand thousand thousand 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 Year 2 Year 4 Year 5 Year 3 Year/Quarter 500 T 450 400 350 300 250 200 150- 100 50 0 1 2 3 4 Year 1 1 2 3 4 Year 2 1 2 3 4 1 2 3 4 1 2 3 4 Year 4 Year 5 Year 3 Year/Quarter (b) Use the following dummy variables to develop an estimated regression equation for the time series data (in $1,000s) to account for seasonal effects in the data. x₁ = 1 if quarter 1, 0 otherwise; x₂ = 1 if quarter 2, 0 otherwise; x3 = 1 if quarter 3, 0 otherwise. ✓ 500 450 400 350 300 250 200 150 100 50 0 MA 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 Year 1 Year 2 Year 4 Year 5 Year 3 Year/Quarter Ⓡ Activate Windows Go to Settings to activate Windows.
Transcribed Image Text:A construction company 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 $298,000 in the first year of operation to $1,069,000 in the most recent year. The following data show the quarterly sales revenue in thousands of dollars. Quarter O 1 3 4 500- 450 400- 0 350- 300+ 250+ 200- 150- 100- 50 (a) Construct a time series plot. Year 1 15 100 170 1 2 3 4 Year 1 13 quarter 3 forecast quarter 4 forecast Year 2 32 M 136 240 $ $ 26 $ $ Year 3 70 155 321 1 2 3 4 1 2 3 4 Year 2 Year 3 Year/Quarter 48 Year 4 1 2 3 4 Year 4 87 202 379 82 Year 5 1 2 3 4 Year 5 171 282 440 176 500 T 450 400+ What type of pattern exists in the data? O The time series plot shows only a linear trend. O The time series plot shows neither a linear trend nor seasonal effects. O The time series plot shows only seasonal effects. The time series plot shows both a linear trend and seasonal effects. 350+ 300+ 250- 200- 150- 100 50 0- 1 2 3 4 Year 1 Based only on the seasonal effects in the data, compute estimates of quarterly sales (in $1,000s) for year 6. quarter 1 forecast thousand quarter 2 forecast thousand thousand thousand 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 Year 2 Year 4 Year 5 Year 3 Year/Quarter 500 T 450 400 350 300 250 200 150- 100 50 0 1 2 3 4 Year 1 1 2 3 4 Year 2 1 2 3 4 1 2 3 4 1 2 3 4 Year 4 Year 5 Year 3 Year/Quarter (b) Use the following dummy variables to develop an estimated regression equation for the time series data (in $1,000s) to account for seasonal effects in the data. x₁ = 1 if quarter 1, 0 otherwise; x₂ = 1 if quarter 2, 0 otherwise; x3 = 1 if quarter 3, 0 otherwise. ✓ 500 450 400 350 300 250 200 150 100 50 0 MA 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 Year 1 Year 2 Year 4 Year 5 Year 3 Year/Quarter Ⓡ Activate Windows Go to Settings to activate Windows.
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