Essentials Of Business Analytics
Essentials Of Business Analytics
1st Edition
ISBN: 9781285187273
Author: Camm, Jeff.
Publisher: Cengage Learning,
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Chapter 5, Problem 23P

Consider the following time series data:

Chapter 5, Problem 23P, Consider the following time series data:

Construct a time series plot. What type of pattern exists

  1. a. Construct a time series plot. What type of pattern exists in the data?
  2. b. Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data: Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 if quarter 2. 0 otherwise; Qtr3 = 1 if quarter 3, 0 otherwise.
  3. c. Compute the quarterly forecasts for next year based on the model you developed in part (b).
  4. 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.
  5. e. Compute the quarterly forecasts for next year based on the model you developed in part (d).
  6. f. Is the model you developed in part (b) or the model you developed in part (d) more effective? Justify your answer.
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Consider the following time series data. Quarter Year 1  Year 2    Year 3 1            4           6           7 2            2           3           6 3           3            5           6 4           5            7           8   Choose the correct time series plot. (i)   (ii)   (iii)   (iv)   - Plot (iii) What type of pattern exists in the data?- Horizontal Pattern with Seasonality  Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. Qtr1 = 1 if Quarter 1, 0 otherwise; Qtr2 = 1 if Quarter 2, 0 otherwise; Qtr3 = 1 if Quarter 3, 0 otherwise. If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300)Value = fill in the blank 3 + fill in the blank 4 Qtr1 + fill in the blank 5 Qtr2 + fill in the blank 6 Qtr3 + fill in the blank 7 t Compute the quarterly forecasts for next year. If…
Consider the following time series data.   Quarter Year 1 Year 2 Year 3 1 4 6 7 2 0 1 4 3 3 5 6 4 5 7 8     (a) Choose the correct time series plot.   (i)   (ii)   (iii)   (iv)         What type of pattern exists in the data?         (b) Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data: Qtr1 = 1 if Quarter 1, 0 otherwise; Qtr2 = 1 if Quarter 2, 0 otherwise; Qtr3 = 1 if Quarter 3, 0 otherwise.   If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank (Example: -300). If the constant is "1" it must be entered in the box. Do not round intermediate calculation.   ŷ =   +  Qtr1 +  Qtr2 +  Qtr3     (c) Compute the quarterly forecasts for next year based on the model you developed in part (b).   If required, round your answers to three decimal places. Do not round…
Consider the following time series:   Quarter Year 1 Year 2 Year 3 1 66 63 57 2 48 40 50 3 59 61 54 4 73 76 67     (a) Choose a time series plot.   (i)   (ii)   (iii)   (iv)         What type of pattern exists in the data? Is there an indication of a seasonal pattern?     (b) Use a multiple linear regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data: Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 if quarter 2, 0 otherwise; Qtr3 = 1 if quarter 3, 0 otherwise. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank (Example: -300).   ŷ = ?? + ?? Qtr1 +??  Qtr2 + ?? Qtr3 (c) Compute the quarterly forecasts for next year.   Year Quarter Ft 4 1   4 2   4 3   4 4
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