Practical Operations Management
2nd Edition
ISBN: 9781939297136
Author: Simpson
Publisher: HERCHER PUBLISHING,INCORPORATED
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Chapter 4, Problem 1.2Q
Summary Introduction
Interpretation: The
Concept Introduction: The following formula will be used to calculate the forecast value −
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An analyst must decide between two different forecasting techniques for weekly sales of roller blades: a linear trend equation and the
naive approach. The linear trend equation is F̟ = 124 + 2.1t, and it was developed using data from periods 1 through 10. Based on data
for periods 11 through 20 as shown in the table, which of these two methods has the greater accuracy if MAD and MSE are used?
(Round your intermediate calculations and final answers to 2 decimal places.)
t
Units Sold
11
144
12
146
13
152
14
142
15
152
16
149
17
152
18
154
19
20
157
164
Click here for the Excel Data File
MAD (Naive)
5.11
MAD (Linear)
5.49
MSE (Naive)
MSE (Linear)
Chapter 4 Solutions
Practical Operations Management
Ch. 4 - Prob. 1DQCh. 4 - Prob. 2DQCh. 4 - Prob. 3DQCh. 4 - Prob. 4DQCh. 4 - Prob. 1PCh. 4 - Prob. 2PCh. 4 - Prob. 3PCh. 4 - Prob. 4PCh. 4 - Prob. 5PCh. 4 - Prob. 6P
Ch. 4 - Prob. 7PCh. 4 - Prob. 8PCh. 4 - Prob. 9PCh. 4 - Prob. 10PCh. 4 - Prob. 11PCh. 4 - Prob. 12PCh. 4 - Prob. 13PCh. 4 - Prob. 14PCh. 4 - Prob. 15PCh. 4 - Prob. 16PCh. 4 - Prob. 17PCh. 4 - Prob. 18PCh. 4 - Prob. 19PCh. 4 - Prob. 20PCh. 4 - Prob. 21PCh. 4 - Prob. 22PCh. 4 - Prob. 23PCh. 4 - Prob. 24PCh. 4 - Prob. 25PCh. 4 - Prob. 26PCh. 4 - Prob. 27PCh. 4 - Prob. 28PCh. 4 - Prob. 29PCh. 4 - Prob. 30PCh. 4 - Prob. 31PCh. 4 - Prob. 32PCh. 4 - Prob. 1.1QCh. 4 - Prob. 1.2QCh. 4 - Prob. 1.3QCh. 4 - Prob. 1.4QCh. 4 - Prob. 2.1QCh. 4 - Prob. 2.2QCh. 4 - Prob. 2.3QCh. 4 - Prob. 2.4QCh. 4 - Prob. 3.1QCh. 4 - Prob. 3.2QCh. 4 - Prob. 3.3Q
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- The 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_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_forwardThe owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?arrow_forward
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