(b) Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. (Round your numerical values to three decimal places.) x, - 1 if quarter 1, 0 otherwise; x, = 1 if quarter 2, 0 otherwise; x, = 1 if quarter 3, 0 otherwise 9- 7.667 – 3-xl – 5-x2-4.x3 (c) Compute the quarterly forecasts for the next year based on the model you developed in part (b). (Round your answers to two decimal places.) quarter 1 forecast 4.67 quarter 2 forecast 2.67 quarter 3 forecast 3.67 quarter 4 forecast 7.67 (d) Use a multiple regression model to develop an equation to account for trend and seasonal effects in the data. Use the dummy variables you developed in part (b) to capture seasonal effects and create a variable t such that t = 1 for quarter 1 in year 1, t = 2 for quarter 2 in year 1, . t = 12 for quarter 4 in year 3. (Round your numerical values to three decimal places.)

(b) Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. (Round your numerical values to three decimal places.) x, - 1 if quarter 1, 0 otherwise; x, = 1 if quarter 2, 0 otherwise; x, = 1 if quarter 3, 0 otherwise 9- 7.667 – 3-xl – 5-x2-4.x3 (c) Compute the quarterly forecasts for the next year based on the model you developed in part (b). (Round your answers to two decimal places.) quarter 1 forecast 4.67 quarter 2 forecast 2.67 quarter 3 forecast 3.67 quarter 4 forecast 7.67 (d) Use a multiple regression model to develop an equation to account for trend and seasonal effects in the data. Use the dummy variables you developed in part (b) to capture seasonal effects and create a variable t such that t = 1 for quarter 1 in year 1, t = 2 for quarter 2 in year 1, . t = 12 for quarter 4 in year 3. (Round your numerical values to three decimal places.)

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter6: Exponential And Logarithmic Functions

Section6.7: Exponential And Logarithmic Models

Problem 2SE: Is carbon dating? Why does it work? Give an example in which carbon dating would be useful.

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Question

Please answer part b and d

(b) Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. (Round your numerical values to
three decimal places.)
X, = 1 if quarter 1, 0 otherwise; x, =1 if quarter 2, 0 otherwise; x, = 1 if quarter 3, 0 otherwise
9 =7.667 – 3.xl – 5-x2 – 4·x3
(c) Compute the quarterly forecasts for the next year based on the model you developed in part (b). (Round your answers to two decimal places.)
quarter 1 forecast
4.67
quarter 2 forecast
2.67
quarter 3 forecast
3.67
quarter 4 forecast
7.67
(d) Use a multiple regression model to develop an equation to account for trend and seasonal effects in the data. Use the dummy variables you developed in part (b) to
capture seasonal effects and create a variable t such that t = 1 for quarter 1 in year 1, t = 2 for quarter 2 in year 1, ..t = 12 for quarter 4 in year 3. (Round your
numerical values to three decimal places.)
Marc
n 3.

Consider the following time series data.
Quarter
Year 1
Year 2
Year 3
1
3
5
1
2
4
6.
8.
4.

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