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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
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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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
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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