A statistical program is recommended. Consider the following time series data. Quarter Year 1 Year 2 Year 3 1 13 15 16 2 11 12 15 3 12 14 15 4 14 16 17 (a) Construct a time series plot. о Time Series Value Time Series Value 18 17 16 15 13 12 11 Time Series Value 18- 17 16- 15 14 13 12 10 10 9 9 1 2 3 4 1 2 3 4 1 2 3 4 Year 1 Year 3 17 18 17 16 15 14 13 11 10- 9- Year 2 Year/Quarter 1 2 3 4 1 2 3 4 1 2 3 4 Year 1 Year 2 Year/Quarter Year 3 Time Series Value 18 8768 17. 16+ 15 14 13 12 10 9 ww 1 2 3 4 1 2 3 4 1 2 3 4 Year 1 Year 2 Year/Quarter Year 3 1 2 3 4 1 2 3 4 1 2 3 4 Year 1 Year 2 Year/Quarter Year 3 What type of pattern exists in the data? The time series plot shows a horizontal pattern, but there is also a seasonal pattern in the data. The time series plot shows a linear trend and no seasonal pattern in the data. The time series plot shows a linear trend and a seasonal pattern in the data. The time series plot shows a horizontal pattern and no seasonal pattern in the data. (b) Use the following dummy variables to develop an estimated regression equation to account for any seasonal and linear trend effects in the data. (Round your numerical values to two decimal places.) x₁ = 1 if quarter 1, 0 otherwise; x2 = 1 if quarter 2, 0 otherwise; x3 = 1 if quarter 3, 0 otherwise (c) Compute the quarterly forecasts for next year. (Round your answers to one decimal place.) quarter 1 forecast quarter 2 forecast quarter 3 forecast quarter 4 forecast
A statistical program is recommended. Consider the following time series data. Quarter Year 1 Year 2 Year 3 1 13 15 16 2 11 12 15 3 12 14 15 4 14 16 17 (a) Construct a time series plot. о Time Series Value Time Series Value 18 17 16 15 13 12 11 Time Series Value 18- 17 16- 15 14 13 12 10 10 9 9 1 2 3 4 1 2 3 4 1 2 3 4 Year 1 Year 3 17 18 17 16 15 14 13 11 10- 9- Year 2 Year/Quarter 1 2 3 4 1 2 3 4 1 2 3 4 Year 1 Year 2 Year/Quarter Year 3 Time Series Value 18 8768 17. 16+ 15 14 13 12 10 9 ww 1 2 3 4 1 2 3 4 1 2 3 4 Year 1 Year 2 Year/Quarter Year 3 1 2 3 4 1 2 3 4 1 2 3 4 Year 1 Year 2 Year/Quarter Year 3 What type of pattern exists in the data? The time series plot shows a horizontal pattern, but there is also a seasonal pattern in the data. The time series plot shows a linear trend and no seasonal pattern in the data. The time series plot shows a linear trend and a seasonal pattern in the data. The time series plot shows a horizontal pattern and no seasonal pattern in the data. (b) Use the following dummy variables to develop an estimated regression equation to account for any seasonal and linear trend effects in the data. (Round your numerical values to two decimal places.) x₁ = 1 if quarter 1, 0 otherwise; x2 = 1 if quarter 2, 0 otherwise; x3 = 1 if quarter 3, 0 otherwise (c) Compute the quarterly forecasts for next year. (Round your answers to one decimal place.) quarter 1 forecast quarter 2 forecast quarter 3 forecast quarter 4 forecast
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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Transcribed Image Text:A statistical program is recommended.
Consider the following time series data.
Quarter Year 1 Year 2 Year 3
1
13
15
16
2
11
12
15
3
12
14
15
4
14
16
17
(a) Construct a time series plot.
о
Time Series Value
Time Series Value
18
17
16
15
13
12
11
Time Series Value
18-
17
16-
15
14
13
12
10
10
9
9
1 2 3 4 1 2 3 4 1 2 3 4
Year 1
Year 3
17
18
17
16
15
14
13
11
10-
9-
Year 2
Year/Quarter
1 2 3 4 1 2 3 4 1 2 3 4
Year 1
Year 2
Year/Quarter
Year 3
Time Series Value
18
8768
17.
16+
15
14
13
12
10
9
ww
1 2 3 4 1 2 3 4 1 2 3 4
Year 1
Year 2
Year/Quarter
Year 3
1 2 3 4 1 2 3 4 1 2 3 4
Year 1
Year 2
Year/Quarter
Year 3
What type of pattern exists in the data?
The time series plot shows a horizontal pattern, but there is also a seasonal pattern in the data.
The time series plot shows a linear trend and no seasonal pattern in the data.
The time series plot shows a linear trend and a seasonal pattern in the data.
The time series plot shows a horizontal pattern and no seasonal pattern in the data.
(b) Use the following dummy variables to develop an estimated regression equation to account for any seasonal and linear
trend effects in the data. (Round your numerical values to two decimal places.)
x₁ = 1 if quarter 1, 0 otherwise; x2 = 1 if quarter 2, 0 otherwise; x3 = 1 if quarter 3, 0 otherwise
(c) Compute the quarterly forecasts for next year. (Round your answers to one decimal place.)
quarter 1 forecast
quarter 2 forecast
quarter 3 forecast
quarter 4 forecast
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