Practical Management Science
6th Edition
ISBN: 9781337406659
Author: WINSTON, Wayne L.
Publisher: Cengage,
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Textbook Question
Chapter 13.7, Problem 28P
The file P13_28.xlsx contains monthly retail sales of U.S. liquor stores.
- a. Is seasonality present in these data? If so, characterize the seasonality pattern.
- b. Use Winters’ method to
forecast this series with smoothing constants α = β = 0.1 and γ = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?
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Use Holt-Winter’s multiplicative method to create a seasonal model that minimizes the MSE for the data set. Use Solver to determine the optimal values of alpha, beta, and gamma. We can initialize these variables to 0.a. What are the optimal values of alpha, beta, and gamma?b. What are the forecasts for each of the next 6 months using this technique?
Year
Month
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COGS
1
1
1
21637
2
2
25506
3
3
26594
4
4
21933
5
5
23117
6
6
26979
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7
28337
8
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28926
9
9
25015
10
10
28196
11
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24170
12
12
25004
2
1
13
24575
2
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35848
3
15
35217
4
16
32759
5
17
36542
6
18
36109
7
19
40279
8
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37816
9
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37814
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23
39359
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24
34151
3
1
25
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2
26
50664
3
27
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4
28
45382
5
29
43981
6
30
41276
7
31
44284
8
32
46920
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43187
Use Holt-Winter’s additive method to create a seasonal model that minimizes the MSE for the data set. Use Solver to determine the optimal values of alpha, beta, and gamma. We can initialize those variables to 0.a. What are the optimal values of alpha, beta, and gamma?b. What are the forecasts for each of the next 6 months using this technique?
Year
Month
Time Period
COGS
1
1
1
21637
2
2
25506
3
3
26594
4
4
21933
5
5
23117
6
6
26979
7
7
28337
8
8
28926
9
9
25015
10
10
28196
11
11
24170
12
12
25004
2
1
13
24575
2
14
35848
3
15
35217
4
16
32759
5
17
36542
6
18
36109
7
19
40279
8
20
37816
9
21
36338
10
22
37814
11
23
39359
12
24
34151
3
1
25
39862
2
26
50664
3
27
48533
4
28
45382
5
29
43981
6
30
41276
7
31
44284
8
32
46920
9
33
47469
10
34
43187
Paraphrase this one. Analyze and elaborate in 120 words.
Time series analysis is used for non-stationary data—things that are constantly fluctuating over time or are affected by time. Industries like finance, retail, and economics frequently use time series analysis because currency and sales are always changing. Stock market analysis is an excellent example of time series analysis in action, especially with automated trading algorithms. Likewise, time series analysis is ideal for forecasting weather changes, helping meteorologists predict everything from tomorrow’s weather report to future years of climate change. Examples of time series analysis in action include:
Weather data
Rainfall measurements
Temperature readings
Heart rate monitoring (EKG)
Brain monitoring (EEG)
Quarterly sales
Stock prices
Automated stock trading
Industry forecasts
Interest rates
Chapter 13 Solutions
Practical Management Science
Ch. 13.3 - The file P13_01.xlsx contains the monthly number...Ch. 13.3 - The file P13_02.xlsx contains five years of...Ch. 13.3 - The file P13_03.xlsx contains monthly data on...Ch. 13.3 - The file P13_04.xlsx lists the monthly sales for a...Ch. 13.3 - Management of a home appliance store wants to...Ch. 13.3 - Do the sales prices of houses in a given community...Ch. 13.3 - Prob. 7PCh. 13.3 - The management of a technology company is trying...Ch. 13.3 - Prob. 9PCh. 13.3 - Sometimes curvature in a scatterplot can be fit...
Ch. 13.4 - Prob. 12PCh. 13.4 - A trucking company wants to predict the yearly...Ch. 13.4 - An antique collector believes that the price...Ch. 13.4 - Stock market analysts are continually looking for...Ch. 13.4 - Suppose that a regional express delivery service...Ch. 13.4 - The owner of a restaurant in Bloomington, Indiana,...Ch. 13.6 - The file P13_19.xlsx contains the weekly sales of...Ch. 13.6 - The file P13_20.xlsx contains the monthly sales of...Ch. 13.6 - The file P13_21.xlsx contains the weekly sales of...Ch. 13.6 - The file P13_22.xlsx contains total monthly U.S....Ch. 13.7 - You have been assigned to forecast the number of...Ch. 13.7 - Simple exponential smoothing with = 0.3 is being...Ch. 13.7 - The file P13_25.xlsx contains the quarterly...Ch. 13.7 - The file P13_26.xlsx contains the monthly number...Ch. 13.7 - The file P13_27.xlsx contains yearly data on the...Ch. 13.7 - The file P13_28.xlsx contains monthly retail sales...Ch. 13.7 - The file P13_29.xlsx contains monthly time series...Ch. 13.7 - A version of simple exponential smoothing can be...Ch. 13 - Prob. 31PCh. 13 - Prob. 32PCh. 13 - Management of a home appliance store would like to...Ch. 13 - A small computer chip manufacturer wants to...Ch. 13 - The file P13_35.xlsx contains the amount of money...Ch. 13 - Prob. 36PCh. 13 - Prob. 37PCh. 13 - Prob. 39PCh. 13 - The Baker Company wants to develop a budget to...Ch. 13 - Prob. 41PCh. 13 - The file P13_42.xlsx contains monthly data on...Ch. 13 - Prob. 43PCh. 13 - Prob. 44PCh. 13 - Prob. 45PCh. 13 - Prob. 46PCh. 13 - Prob. 49P
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Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, operations-management and related others by exploring similar questions and additional content below.Similar questions
- The file P13_21.xlsx contains the weekly sales of rakes at a hardware store for a two-year period. Use the moving averages method, with spans of your choice, to forecast sales for the next 30 weeks. Does this method appear to track sales well? If not, what might be the reason?arrow_forwardThe file P13_01.xlsx contains the monthly number of airline tickets sold by a travel agency. a. Does a linear trend appear to fit these data well? If so, estimate and interpret the linear trend model for this time series. Also, interpret the R2 and se values. b. Provide an indication of the typical forecast error generated by the estimated model in part a. c. Is there evidence of some seasonal pattern in these sales data? If so, characterize the seasonal pattern.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_forward
- The file P13_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel Agency. 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. Does it make much of an improvement over the model in part b?arrow_forwardThe file P13_20.xlsx contains the monthly sales of iPod cases at an electronics store for a two-year period. Use the moving averages method, with spans of your choice, to forecast sales for the next six months. Does this method appear to track sales well? If not, what might be the reason?arrow_forwardThe file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building materials (which includes retail sales of building materials, hardware and garden supply stores, and mobile home dealers). a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?arrow_forward
- The file P13_25.xlsx contains the quarterly numbers of applications for home mortgage loans at a branch office of Northern Central Bank. 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. Does it make much of an improvement over the model in part b? Is it guaranteed to produce better forecasts for the future?arrow_forwardStock market analysts are continually looking for reliable predictors of stock prices. Consider the problem of modeling the price per share of electric utility stocks (Y). Two variables thought to influence this stock price are return on average equity (X1) and annual dividend rate (X2). The stock price, returns on equity, and dividend rates on a randomly selected day for 16 electric utility stocks are provided in the file P13_15.xlsx. Estimate a multiple regression equation using the given data. Interpret each of the estimated regression coefficients. Also, interpret the standard error of estimate and the R-square value for these data.arrow_forwardThe file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six months of observations for validation purposes, use the method of moving averages with a carefully chosen span to forecast U.S. retail sales in the next year. Comment on the performance of your model. What makes this time series more challenging to forecast?arrow_forward
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