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 $1,079,000 in the most recent year. The following data show the quarterly sales revenue in thousands of dollars. Quarter Year 1 Year 2 Year 3 20 37 75 95 131 175 O 500 450 1 (a) Construct a time series plot. 400 2 330 3 4 8 245 21 quarter 3 forecast quarter 4 forecast 150 326 43 400 400+ 350+ 300 300 300 250 250 250 250 200 My www ~ L 200 200 200 150 150 150 150 100 100- 100- 30 50 30 12341234123412341234 12341234123412341234 12341234123412341234 12341234123412341234 Year 1 Year 2 Year 3 Year 4 Year 5 Year 1 Year 2 Year 3 Year 4 Year 5 Year 1 Year 2 Year 3 Year 4 Year 5 Year 1 Year 2 Year 3 Year 4 Year 5 Year/Quarter Year Quarter Year/Quarter Year Quarter s[ $[ $ $ Year 4 Year 5 92 176 277 445 197 384 77 181 500 450 What type of pattern exists in the data? The time series plot shows both a linear trend and seasonal effects. O The time series plot shows only seasonal effects. O The time series plot shows only a linear trend. O The time series plot shows neither a linear trend nor seasonal effects. 350 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 500 450 400 350 300 100 50 500 O (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. x₂-1 if quarter 1, 0 otherwise; x₂-1 if quarter 2, 0 otherwise; x₂-1 if quarter 3, 0 otherwise. 9- 450 Ⓡ Activate Windows Go to Settings to activate Windows
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 $1,079,000 in the most recent year. The following data show the quarterly sales revenue in thousands of dollars. Quarter Year 1 Year 2 Year 3 20 37 75 95 131 175 O 500 450 1 (a) Construct a time series plot. 400 2 330 3 4 8 245 21 quarter 3 forecast quarter 4 forecast 150 326 43 400 400+ 350+ 300 300 300 250 250 250 250 200 My www ~ L 200 200 200 150 150 150 150 100 100- 100- 30 50 30 12341234123412341234 12341234123412341234 12341234123412341234 12341234123412341234 Year 1 Year 2 Year 3 Year 4 Year 5 Year 1 Year 2 Year 3 Year 4 Year 5 Year 1 Year 2 Year 3 Year 4 Year 5 Year 1 Year 2 Year 3 Year 4 Year 5 Year/Quarter Year Quarter Year/Quarter Year Quarter s[ $[ $ $ Year 4 Year 5 92 176 277 445 197 384 77 181 500 450 What type of pattern exists in the data? The time series plot shows both a linear trend and seasonal effects. O The time series plot shows only seasonal effects. O The time series plot shows only a linear trend. O The time series plot shows neither a linear trend nor seasonal effects. 350 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 500 450 400 350 300 100 50 500 O (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. x₂-1 if quarter 1, 0 otherwise; x₂-1 if quarter 2, 0 otherwise; x₂-1 if quarter 3, 0 otherwise. 9- 450 Ⓡ Activate Windows Go to Settings to activate Windows
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; ... 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
seasonal effects and any linear trend in the time series. (Round your numerical values to one decimal place.)
Ŷ₂ =
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
quarter 2 forecast
$
quarter 3 forecast
$
quarter 4 forecast
$
thousand
thousand
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 $1,079,000 in the most recent year. The following data show the quarterly sales revenue
in thousands of dollars.
Quarter Year 1
O
500
450
400
350
(a) Construct a time series plot.
200
1
2
150
3
300+
250-
100
4
50
20
95
175
Year 2
quarter 2 forecast
quarter 3 forecast
quarter 4 forecast
37
131
245
21
Year 3
75
150
326
500-
450
400-
350
300
250
200
Mm WW NU
150-
100-
50
0
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
Year 3 Year 4 Year 5
Year/Quarter
Year 1 Year 2
Year 1
Year 2
Year 4
Year 5
Year 3
Year/Quarter
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 3 Year 4 Year 5
Year/Quarter
43
Year 4
92
197
384
thousand
thousand
77
thousand
thousand
Year 5
176
277
445
181
500
450
400+
350
300-
250-
200-
What type of pattern exists in the data?
The time series plot shows both a linear trend and seasonal effects.
O The time series plot shows only seasonal effects.
O The time series plot shows only a linear trend.
O The time series plot shows neither a linear trend nor seasonal effects.
150-
100-
50
Based only on the seasonal effects in the data, compute estimates of quarterly sales (in $1,000s) for year 6.
quarter 1 forecast
$
$
$
$
0
500-
450
400-
350
300-
250-
200-
150-
100
50
0
(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.
ŷ=
Ⓡ✓
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
Year 5
Year 1 Year 2 Year 3 Year 4
Year/Quarter
Ⓡ
Activate Windows
Go to Settings to activate Windows.
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