Essentials of Business Analytics (MindTap Course List)
2nd Edition
ISBN: 9781305627734
Author: Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson
Publisher: Cengage Learning
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Textbook Question
Chapter 8, Problem 18P
Consider the following time series:
- a. Construct a time series plot. What type of pattern exists in the data?
- b. Use simple linear
regression analysis to find the parameters for the line that minimizes MSE for this time series. - c. What is the forecast for t = 8?
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Students have asked these similar questions
Assume that the current date is February 1, 2021. The linear regression model was applied to a monthly time series data based on the last 24 months' sales. (From January 2019 through December 2020). The following partial computer output summarizes the results.
Coefficient
Estimate
t
Intercept
4.3
2.07
Slope
1.6
2.98
Determine the predicted sales for February.
Consider the following time series:
Quarter
Year 1
Year 2
Year 3
80
74
65
69
61
51
48
50
43
68
71
82
a. Construct a time-series plot. What type of pattern exists in the data? Is there an indication
of a seasonal pattern?
b. Use multiple linear regression model with dummy variables as follows to develop an
equation to account for seasonal effects in the data. Qtrl = 1 if quarter 1,0 else; Qtr2 = 1 if
quarter 2,0 else; Qtr3 = 1 if quarter 3,0 else.
c. Compute the quarterly forecasts for next year.
The quarterly sales data (number of copies sold) for a college textbook over the past three years follow.
a) Construct a time series plot. What type of pattern exists in the data?
b) Use a 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.
c) Compute the quarterly forecasts for next year.
d) Let t=1 to refer to the observation in quarter 1 of year 1; t=2 to refer to the observation in quarter 2 of year 1; ...; and t=12 to refer to the observation in quarter 4 of year 3. Using the dummy variables defined in part (b) and also using t, develop an equation to account for seasonal effects and any linear trend in the time series. Based upon the seasonal effects in the data and linear trend, compute the quarterly forecasts for next year.
Chapter 8 Solutions
Essentials of Business Analytics (MindTap Course List)
Ch. 8 - Consider the following time series data:
Using...Ch. 8 - Refer to the time series data in Problem 1. Using...Ch. 8 - Problems 1 and 2 used different forecasting...Ch. 8 - Consider the following time series data:
Compute...Ch. 8 - Consider the following time series...Ch. 8 - Consider the following time series...Ch. 8 - Refer to the gasoline sales time series data in...Ch. 8 - Prob. 8PCh. 8 - Prob. 9PCh. 8 - Prob. 10P
Ch. 8 - For the Hawkins Company, the monthly percentages...Ch. 8 - Corporate triple A bond interest rates for 12...Ch. 8 - The values of Alabama building contracts (in...Ch. 8 - The following time series shows the sales of a...Ch. 8 - Prob. 15PCh. 8 - The following table reports the percentage of...Ch. 8 - Consider the following time series: a. Construct a...Ch. 8 - Consider the following time series:
Construct a...Ch. 8 - Because of high tuition costs at state and private...Ch. 8 - The Seneca Children’s Fund (SCF) is a local...Ch. 8 - The president of a small manufacturing firm is...Ch. 8 - Consider the following time series: a. Construct a...Ch. 8 - Consider the following time series...Ch. 8 - The quarterly sales data (number of copies sold)...Ch. 8 - Prob. 25PCh. 8 - South Shore Construction builds permanent docks...Ch. 8 - Hogs & Dawgs is an ice cream parlor on the border...Ch. 8 - Donna Nickles manages a gasoline station on the...Ch. 8 - The Vintage Restaurant, on Captiva Island near...
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