(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
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
100%

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
$
$

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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