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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Chapter 8, Problem 17P
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 = 6?
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Students have asked these similar questions
1. 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.
Which of the following time series forecasting methods would not be used to forecast seasonal data?
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 minimizesMSE for this time series.c. What is the forecast for t 5 6?
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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- 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 4arrow_forwardconsider the following time series data.Month 1 2 3 4 5 6 7Value 24 13 20 12 19 23 15a. compute MSe using the most recent value as the forecast for the next period. Whatis the forecast for month 8?b. compute MSe using the average of all the data available as the forecast for the nextperiod. What is the forecast for month 8?c. Which method appears to provide the better forecast?arrow_forwardConsider the following time series data. Week 1 2 3 4 5 6 Value 18 13 16 11 17 14 Construct a time series plot. What type of pattern exist in the data? Develop a three-week moving average for this time series. Compute MSE and forecast for week 7. Use a = 0.2 to compute the exponential smoothing values for the time series. Compute MSE and forecast for week 7.arrow_forward
- Consider the following time series. Quarter Year 1 Year 2 Year 3 1 71 68 62 2 49 41 51 3 58 60 53 4 78 81 72 Construct a time series plot. What type of pattern exists in the data? 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. Compute the quarterly forecast for the next year.arrow_forwardWhich of the time series forecasting methods would not be used to forecast seasonal data?arrow_forwardConsider the following time series data Week 1 2 3 4 5 6 Value 18 13 16 11 17 14 a. Construct a time series plot. What type of pattern exists in the data?b. Develop the three-week moving average forecasts for this time series. compute MSE and a forecast for week 7.c. Use α = .2 to compute the exponential smoothing forecasts for the time series.Compute MSE and a forecast for week 7.d. Compare the three-week moving average approach with the exponentialsmoothing approach using α = .2. Which appears to provide more accurate forecasts based on MSE? explain.e. Use a smoothing constant of α = .4 to compute the exponential smoothing forecasts. does a smoothing constant of .2 or .4 appear to provide more accurate forecasts based on MSE? explain.arrow_forward
- Compute the forecasted values for Yt for July and August in 2020 by using the modelsstated in (c) and (d)arrow_forwardBelow you are given the first five values of a quarterly time series. The multiplicative model is appropriate and a four-quarter moving average will be used. Year Quarter Time Series Value Yt 1 1 36 2 24 3 16 2 4 20 1 44 An estimate of the combined trend-cycle component (T2Ct) for Quarter 3 of Year 1 (used for estimating the de-trended values), when a four-quarter moving average is used, is a. 24. b. 26. c. 28. d. 25.arrow_forwardThe following plots have been obtained for a time series. a) Suggest an appropriate ARIMA model. b) The following ARIMA output has been obtained from R. Based on this output, which model would you recommend for forecasting?arrow_forward
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