EBK PRACTICAL MANAGEMENT SCIENCE
5th Edition
ISBN: 9780100655065
Author: ALBRIGHT
Publisher: YUZU
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Chapter 14.6, Problem 20P
Summary Introduction
To forecast: The sales for the next six months using moving average.
Introduction:
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Answer in step by step with explanation.
Year
Season
Sales
2018
Winter
40
2018
Spring
29
2018
Summer
31
2018
Fall
40
2019
Winter
102
2019
Spring
87
2019
Summer
96
2019
Fall
132
2020
Winter
105
2020
Spring
93
2020
Summer
105
2020
Fall
117
2021
Winter
141
2021
Spring
39
2021
Summer
114
2021
Fall
123
What is the slope of the trend equation obtained by linear regression? Round to two decimal digits.
What is the intercept of the trend equation obtained by linear regression? Round to two decimal digits.
What is the seasonal index for Spring? Round to two decimal digits.
The quarter number for Winter of 2018 is 1. What is the quarter number for Spring of 2025?
What is the trend based forecast for Spring of 2025. Round to a whole number.
What is the seasonally adjusted trend based forecast for Spring of 2025?
Please do not use excel to find the slope and intercept, thank you so much!
Chapter 14 Solutions
EBK PRACTICAL MANAGEMENT SCIENCE
Ch. 14.3 - Prob. 1PCh. 14.3 - Prob. 2PCh. 14.3 - Prob. 3PCh. 14.3 - Prob. 4PCh. 14.3 - Prob. 5PCh. 14.3 - Prob. 6PCh. 14.3 - Prob. 7PCh. 14.3 - Prob. 8PCh. 14.3 - Prob. 9PCh. 14.3 - Prob. 10P
Ch. 14.4 - Prob. 12PCh. 14.4 - Prob. 13PCh. 14.4 - Prob. 14PCh. 14.4 - Prob. 15PCh. 14.4 - Prob. 16PCh. 14.4 - Prob. 17PCh. 14.6 - Prob. 19PCh. 14.6 - Prob. 20PCh. 14.6 - The file P14_21.xlsx contains the weekly sales of...Ch. 14.6 - Prob. 22PCh. 14.7 - Prob. 23PCh. 14.7 - Prob. 24PCh. 14.7 - Prob. 25PCh. 14.7 - Prob. 26PCh. 14.7 - Prob. 27PCh. 14.7 - Prob. 28PCh. 14.7 - Prob. 29PCh. 14.7 - Prob. 30PCh. 14 - Prob. 31PCh. 14 - Prob. 32PCh. 14 - Prob. 33PCh. 14 - Prob. 34PCh. 14 - Prob. 35PCh. 14 - Prob. 36PCh. 14 - Prob. 37PCh. 14 - Prob. 39PCh. 14 - Prob. 40PCh. 14 - Prob. 41PCh. 14 - Prob. 42PCh. 14 - Prob. 43PCh. 14 - Prob. 44PCh. 14 - Prob. 45PCh. 14 - Prob. 46PCh. 14 - Prob. 49P
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- The file P13_02.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the companys suspicion. b. By what percentage are sales increasing each month? c. What is the MAPE for the forecast model in part b? In words, what does it measure? Considering its magnitude, does the model seem to be doing a good job? d. In words, how does the model make forecasts for future months? Specifically, given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months?arrow_forwardThe file P13_19.xlsx contains the weekly sales of a particular brand of paper towels at a supermarket for a one-year period. a. Using a span of 3, forecast the sales of this product for the next 10 weeks with the moving averages method. How well does this method with span 3 forecast the known observations in this series? b. Repeat part a with a span of 10. c. Which of these two spans appears to be more appropriate? Justify your choice.arrow_forwardThe file P13_27.xlsx contains yearly data on the proportion of Americans under the age of 18 living below the poverty level. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Create a chart of the series with the forecasts superimposed from this optimal smoothing constant. Does it make much of an improvement over the model in part b? d. Write a short report to summarize your results. Considering the chart in part c, would you say the forecasts are good?arrow_forward
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