Production and Operations Analysis, Seventh Edition
7th Edition
ISBN: 9781478623069
Author: Steven Nahmias, Tava Lennon Olsen
Publisher: Waveland Press, Inc.
expand_more
expand_more
format_list_bulleted
Concept explainers
Videos
Question
Chapter 2.8, Problem 29P
(a)
Summary Introduction
To determine: The forecast for the total number of people using the S Park for calendar year 2014
Introduction:
(b)
Summary Introduction
To determine: The forecast for total users of S Park for calendar year 2014 using a six-moth moving average
Introduction: A moving average forecast is obtained by computing arithmetic average of the N that is the most recent observation of demand.
(c)
Summary Introduction
To explain: A curve of the most likely shape explaining the use of park by month during the calendar year; forecasting based on the obtained graph and its reassessment.
Introduction: Forecasting is done based on previously observed data. Assuming first six months/periods of 2014 as baseline, forecasted values for rest periods of the year can be attained.
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solution
Students have asked these similar questions
The following gives the number of accidents that occurred on Florida State Highway 101 during the last 4 months:
Month
Jan
Feb
Mar
Apr
Number of Accidents
25
45
60
95
Part 2
Using the
least-squares regression
LOADING...
method, the trend equation for forecasting is (round your responses to two decimal
places):
y
=
+
enter your response herex
Part 3
Using least-squares regression, the forecast for the number of accidents that will occur in the month of May =
enter your response here
accidents (enter your response as a whole number).
A security company had to deploy guards for emergencies multiple times in the last four evenings. The numbers of
emergencies for Monday, Tuesday, Wednesday, and Thursday were 7, 4, 8, and 11, respectively. What would be the
security company's forecast for the number of emergencies on Friday using an exponential smoothing forecasting
approach? (Use \alpha= 0.2 and a forecast for Monday of 10 emergencies)
The manager of the Salem police department motor poolwants to develop a forecast model for annual maintenanceon police cars based on mileage in the past year and age ofthe cars. The following data have been collected for eightdifferent cars:
a. Using Excel develop a multiple regression equationfor these data.b. What is the coefficient of determination for thisregression equation?c. Forecast the annual maintenance cost for a police carthat is four years old and will be driven 10,000 miles inone year.
Chapter 2 Solutions
Production and Operations Analysis, Seventh Edition
Ch. 2.4 - Prob. 1PCh. 2.4 - Prob. 2PCh. 2.4 - Prob. 3PCh. 2.4 - Prob. 4PCh. 2.4 - Prob. 5PCh. 2.4 - Prob. 6PCh. 2.4 - Prob. 7PCh. 2.4 - Prob. 8PCh. 2.4 - Prob. 9PCh. 2.6 - Prob. 10P
Ch. 2.6 - Prob. 11PCh. 2.6 - Prob. 12PCh. 2.6 - Prob. 13PCh. 2.6 - Prob. 14PCh. 2.6 - Prob. 15PCh. 2.7 - Prob. 16PCh. 2.7 - Prob. 17PCh. 2.7 - Prob. 18PCh. 2.7 - Prob. 19PCh. 2.7 - Prob. 20PCh. 2.7 - Prob. 21PCh. 2.7 - Prob. 22PCh. 2.7 - Prob. 23PCh. 2.7 - Prob. 24PCh. 2.7 - Prob. 25PCh. 2.7 - Prob. 26PCh. 2.7 - Prob. 27PCh. 2.8 - Prob. 28PCh. 2.8 - Prob. 29PCh. 2.8 - Prob. 30PCh. 2.8 - Prob. 31PCh. 2.8 - Prob. 32PCh. 2.9 - Prob. 33PCh. 2.9 - Prob. 34PCh. 2.9 - Prob. 35PCh. 2.9 - Prob. 36PCh. 2.9 - Prob. 37PCh. 2.10 - Prob. 38PCh. 2.10 - Prob. 42PCh. 2.10 - Prob. 43PCh. 2.10 - Prob. 44PCh. 2.10 - Prob. 45PCh. 2 - Prob. 47APCh. 2 - Prob. 48APCh. 2 - Prob. 49APCh. 2 - Prob. 50APCh. 2 - Prob. 51APCh. 2 - Prob. 52APCh. 2 - Prob. 53APCh. 2 - Prob. 54APCh. 2 - Prob. 55APCh. 2 - Prob. 56APCh. 2 - Prob. 57APCh. 2 - Prob. 58AP
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
- Under what conditions might a firm use multiple forecasting methods?arrow_forwardThe 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 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_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 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_forwardDo 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_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_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_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
- Suppose 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_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_forwardThe 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_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 Learning
MarketingMarketingISBN: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