OPERATIONS MANAGEMENT CUSTOM ACCESS
11th Edition
ISBN: 9780135622438
Author: KRAJEWSKI
Publisher: PEARSON EDUCATION (COLLEGE)
expand_more
expand_more
format_list_bulleted
Concept explainers
Question
Chapter 8, Problem 22P
Summary Introduction
Interpretation: The trend projection with regression using time series
Concept Introduction: Forecasting refers to the estimation of a future event during a fixed time and trend projection with regression is mean of estimation with the help of regression line and exponential smoothing refers to data smoothing(moving average can be used) for forecasting having a pattern in given data
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solutionStudents have asked these similar questions
nfo
K
The Wellington company wants to develop a simple linear regression model for one of its products. Use the following 12 periods of historical data to develop the
regression equation and use it to forecast the next three periods.
Click the icon to view the historical data for the previous 12 periods.
The simple linear regression line is F₁ =
+ x₁. (Enter your responses rounded to two decimal places and include a minus sign if necessary.)
Find the forecasts for periods 13-15 based on the simple linear regression and fill in the table below (enter your responses rounded to two decimal places).
Period
Forecast
(Ft)
Period
(x)
1
2
3
4
5
6
7
8
9
10
11
12
(y)
905
930
825
774
791
647
656
661
479
669
494
441
X
(x) Fo
13
14
15
Please, I need help with this one too. It is due today.
The following data are for calculator sales in units at an electronics store over the past nine weeks:
Week
Sales
Week
Sales
1
45
53
2
50
7
59
3
44
8.
59
4
51
9
64
5
57
Use trend projection with regression to forecast sales for weeks 10 - 13. What are the error measures (CFE, MSE, o, MAD, and MAPE) for this forecasting procedure? How about ?
Obtain the trend projection with regression forecast for weeks 10 – 13. (Enter your responses rounded to two decimal places.)
Period
Forecast, F,
10
64.81
11
67.06
12
69.31
13
71.56
Obtain the error measures. (Enter your responses rounded to two decimal places.)
CFE
MSE
MAD
МАРЕ
Chapter 8 Solutions
OPERATIONS MANAGEMENT CUSTOM ACCESS
Ch. 8 - Figure 8.9 shows summer air visibility...Ch. 8 - Kay and Michael Passe publish What‘s...Ch. 8 - Demand for oil changes at Garcia’s Garage has...Ch. 8 - Prob. 2PCh. 8 - Ohio Swiss Milk Products manufactures and...Ch. 8 - A manufacturing firm has developed a skills test,...Ch. 8 - The materials handling manager of a manufacturing...Ch. 8 - Marianne Kramer, the owner of Handy Man Rentals,...Ch. 8 - Sales for the past 12 months at Computer Success...Ch. 8 - Bradley’s Copiers sells and repairs photocopy...
Ch. 8 - Consider the sales data for Computer Success given...Ch. 8 - A convenience store recently started to carry a...Ch. 8 - Community Federal Bank in Dothan, Alabama,...Ch. 8 - The number of heart surgeries performed at...Ch. 8 - The following data are for calculator sales in...Ch. 8 - Prob. 14PCh. 8 - Forrest and Dan make boxes of chocolates for which...Ch. 8 - The manager of Alaina’s Garden Center must make...Ch. 8 - The manager of a utility company in the Texas...Ch. 8 - Franklin Tooling, Inc., manufactures specialty...Ch. 8 - Create an Excel spreadsheet on your own that can...Ch. 8 - Prob. 20PCh. 8 - Using the data in Problem 20 and the Time-Series...Ch. 8 - Prob. 22PCh. 8 - Cannister, Inc., specializes in the manufacture of...Ch. 8 - The Midwest Computer Company serves a large number...Ch. 8 - A certain food item at P=0.20 (with a combination...Ch. 8 - Prob. 26PCh. 8 - Prob. 27PCh. 8 - A manufacturing firm seeks to develop a better...Ch. 8 - How much does the forecasting process at Deckers...Ch. 8 - Prob. 2VCCh. 8 - What factors make forecasting at Deckers...Ch. 8 - Prob. 4VCCh. 8 - Prob. 5VCCh. 8 - Comment on the forecasting system being used by...Ch. 8 - Develop your own forecast for bow rakes for each...
Knowledge Booster
Learn more about
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 Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?arrow_forwardThe file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.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
- 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 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_forward
- Do the sales prices of houses in a given community vary systematically with their sizes (as measured in square feet)? Answer this question by estimating a simple regression equation where the sales price of the house is the dependent variable, and the size of the house is the explanatory variable. Use the sample data given in P13_06.xlsx. Interpret your estimated equation, the associated R-square value, and the associated standard error of estimate.arrow_forwardThe 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 = = 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 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_forward
- A small computer chip manufacturer wants to forecast monthly ozperating costs as a function of the number of units produced during a month. The company has collected the 16 months of data in the file P13_34.xlsx. a. Determine an equation that can be used to predict monthly production costs from units produced. Are there any outliers? b. How could the regression line obtained in part a be used to determine whether the company was efficient or inefficient during any particular month?arrow_forwardThe management of a technology company is trying to determine the variable that best explains the variation of employee salaries using a sample of 52 full-time employees; see the file P13_08.xlsx. Estimate simple linear regression equations to identify which of the following has the strongest linear relationship with annual salary: the employees gender, age, number of years of relevant work experience prior to employment at the company, number of years of employment at the company, or number of years of post secondary education. Provide support for your conclusion.arrow_forwardSuppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semifragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P13_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semifragile cargo.arrow_forward
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Recommended textbooks for you
- Practical Management ScienceOperations ManagementISBN:9781337406659Author:WINSTON, Wayne L.Publisher:Cengage,Contemporary MarketingMarketingISBN:9780357033777Author:Louis E. Boone, David L. KurtzPublisher:Cengage LearningMarketingMarketingISBN:9780357033791Author:Pride, William MPublisher:South Western Educational Publishing
Practical Management Science
Operations Management
ISBN:9781337406659
Author:WINSTON, Wayne L.
Publisher:Cengage,
Contemporary Marketing
Marketing
ISBN:9780357033777
Author:Louis E. Boone, David L. Kurtz
Publisher:Cengage Learning
Marketing
Marketing
ISBN:9780357033791
Author:Pride, William M
Publisher:South Western Educational Publishing
Single Exponential Smoothing & Weighted Moving Average Time Series Forecasting; Author: Matt Macarty;https://www.youtube.com/watch?v=IjETktmL4Kg;License: Standard YouTube License, CC-BY
Introduction to Forecasting - with Examples; Author: Dr. Bharatendra Rai;https://www.youtube.com/watch?v=98K7AG32qv8;License: Standard Youtube License