Principles of Operations Management: Sustainability and Supply Chain Management (10th Edition)
10th Edition
ISBN: 9780134181981
Author: Jay Heizer, Barry Render, Chuck Munson
Publisher: PEARSON
expand_more
expand_more
format_list_bulleted
Concept explainers
Videos
Textbook Question
Chapter 4, Problem 48P
Rhonda Clark, a Slippery Rock, Pennsylvania, real estate developer, has devised a regression model to help determine residential housing prices in northwestern Pennsylvania. The model was developed using recent sales in a particular neighborhood. The price (Y) of the house is based on the size (square foot-age = X) of the house. The model is:
The coefficient of correlation for the model is 0.63.
a) Use the model to predict the selling price of a house that is 1,860 square feet.
b) An 1,860-square-foot house recently sold for $95,000. Explain why this is not what the model predicted.
c) If you were going to use multiple regression to develop such a model, what other quantitative variables might you include?
d) What is the value of the coefficient of determination in this problem?
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solution
Chapter 4 Solutions
Principles of Operations Management: Sustainability and Supply Chain Management (10th Edition)
Ch. 4 - Ethical Dilemma We live in a society obsessed with...Ch. 4 - What is a qualitative forecasting model, and when...Ch. 4 - Identify and briefly describe the two general...Ch. 4 - Identify the three forecasting time horizons....Ch. 4 - Briefly describe the steps that are used to...Ch. 4 - A skeptical manager asks what medium-range...Ch. 4 - Explain why such forecasting devices as moving...Ch. 4 - What is the basic difference between a weighted...Ch. 4 - What three methods are used to determine the...Ch. 4 - Research and briefly describe the Delphi...
Ch. 4 - What is the primary difference between a...Ch. 4 - Define time series.Ch. 4 - What effect does the value of the smoothing...Ch. 4 - Explain the value of seasonal indices in...Ch. 4 - Prob. 14DQCh. 4 - In your own words, explain adaptive forecasting.Ch. 4 - Prob. 16DQCh. 4 - Explain, in your own words, the meaning of the...Ch. 4 - Prob. 18DQCh. 4 - Give examples of industries that are affected by...Ch. 4 - Prob. 20DQCh. 4 - Prob. 21DQCh. 4 - CEO John Goodale, at Southern Illinois Power and...Ch. 4 - The following gives the number of pints of type B...Ch. 4 - a) Plot the above data on a graph. Do you observe...Ch. 4 - Refer to Problem 4.2. Develop a forecast for years...Ch. 4 - A check-processing center uses exponential...Ch. 4 - The Carbondale Hospital is considering the...Ch. 4 - The monthly sales for Yazici Batteries, Inc., were...Ch. 4 - The actual demand for the patients at Omaha...Ch. 4 - Daily high temperatures in St. Louis for the last...Ch. 4 - Lenovo uses the ZX-81 chip in some of its laptop...Ch. 4 - Data collected on the yearly registrations for a...Ch. 4 - Use exponential smoothing with a smoothing...Ch. 4 - Prob. 12PCh. 4 - At you can see in the following table, demand for...Ch. 4 - Prob. 14PCh. 4 - Refer to Solved Problem 4.1 on page 144. a) Use a...Ch. 4 - Prob. 16PCh. 4 - Prob. 17PCh. 4 - Prob. 18PCh. 4 - Income at the architectural firm Spraggins and...Ch. 4 - Resolve Problem 4.19 with = .1 and =.8. Using...Ch. 4 - Prob. 21PCh. 4 - Refer to Problem 4.21. Complete the trend-adjusted...Ch. 4 - Prob. 23PCh. 4 - The following gives the number of accidents that...Ch. 4 - In the past, Peter Kelles tire dealership in Baton...Ch. 4 - George Kyparisis owns a company that manufactures...Ch. 4 - Attendance at Orlandos newest Disneylike...Ch. 4 - Prob. 28PCh. 4 - The number of disk drives (in millions) made at a...Ch. 4 - Prob. 30PCh. 4 - Emergency calls to the 911 system of Durham, North...Ch. 4 - Using the 911 call data in Problem 4.31, forecast...Ch. 4 - Storrs Cycles has just started selling the new...Ch. 4 - Boulanger Savings and Loan is proud of its long...Ch. 4 - Mark Gershon, owner of a musical instrument...Ch. 4 - Prob. 44PCh. 4 - Cafe Michigans manager, Gary Stark, suspects that...Ch. 4 - Prob. 46PCh. 4 - The number of auto accidents in Athens, Ohio, is...Ch. 4 - Rhonda Clark, a Slippery Rock, Pennsylvania, real...Ch. 4 - Accountants at the Tucson firm, Larry Youdelman,...Ch. 4 - Prob. 50PCh. 4 - Using the data in Problem 4.30, apply linear...Ch. 4 - Bus and subway ridership for the summer months in...Ch. 4 - Prob. 53PCh. 4 - Dave Fletcher, the general manager of North...Ch. 4 - Sales of tablet computers at Ted Glickmans...Ch. 4 - The following are monthly actual and forecast...Ch. 4 - Develop a forecasting model, justifying its...Ch. 4 - Prob. 2CSCh. 4 - Discuss the schools options.Ch. 4 - Prob. 1.1VCCh. 4 - Prob. 1.2VCCh. 4 - Using Perezs multiple-regression model, what would...Ch. 4 - Prob. 1.4VCCh. 4 - Describe three different forecasting applications...Ch. 4 - What is the role of the POS system in forecasting...Ch. 4 - Justify the use of the weighting system used for...Ch. 4 - Name several variables besides those mentioned in...Ch. 4 - Prob. 2.5VC
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_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_forwardA 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_forward
- The 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_forwardA trucking company wants to predict the yearly maintenance expense (Y) for a truck using the number of miles driven during the year (X1) and the age of the truck (X2, in years) at the beginning of the year. The company has gathered the data given in the file P13_13.xlsx. Note that each observation corresponds to a particular truck. 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_forward
- Stock 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_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_forwardManagement of a home appliance store would like to understand the growth pattern of the monthly sales of Blu-ray disc players over the past two years. Managers have recorded the relevant data in the file P13_33.xlsx. a. Create a scatterplot for these data. Comment on the observed behavior of monthly sales at this store over time. b. Estimate an appropriate regression equation to explain the variation of monthly sales over the given time period. Interpret the estimated regression coefficients. c. Analyze the estimated equations residuals. Do they suggest that the regression equation is adequate? If not, return to part b and revise your equation. Continue to revise the equation until the results are satisfactory.arrow_forward
- An antique collector believes that the price received for a particular item increases with its age and with the number of bidders. The file P13_14.xlsx contains data on these three variables for 32 recently auctioned comparable items. Estimate a multiple regression equation using the given data. Interpret each of the estimated regression coefficients. Is the antique collector correct in believing that the price received for the item increases with its age and with the number of bidders? Interpret the standard error of estimate and the R-square value for these data.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
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Recommended textbooks for you
Practical Management ScienceOperations ManagementISBN:9781337406659Author:WINSTON, Wayne L.Publisher:Cengage,
Practical Management Science
Operations Management
ISBN:9781337406659
Author:WINSTON, Wayne L.
Publisher:Cengage,
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