Operations and Supply Chain Management 9th edition
9th Edition
ISBN: 9781119320975
Author: Roberta S. Russell, Bernard W. Taylor III
Publisher: WILEY
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Chapter 12, Problem 22Q
Define the different components (y, x, a, and b) of a linear regression equation.
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Chapter 12 Solutions
Operations and Supply Chain Management 9th edition
Ch. 12 - List some of the operations and functions in a...Ch. 12 - What is the difference between quantitative...Ch. 12 - Describe the difference between short- and...Ch. 12 - Prob. 4QCh. 12 - Why is accurate forecasting so important to...Ch. 12 - Discuss the relationship between forecasting and...Ch. 12 - Prob. 7QCh. 12 - Describe the Delphi method for forecasting.Ch. 12 - What is the difference between a trend and a cycle...Ch. 12 - How is the moving average method similar to...
Ch. 12 - In the chapter examples for time series methods,...Ch. 12 - What effect on the exponential smoothing model...Ch. 12 - How does adjusted exponential smoothing differ...Ch. 12 - What determines the choice of the smoothing...Ch. 12 - How does the linear trend line forecasting model...Ch. 12 - Of the time series models presented in this...Ch. 12 - What advantages does adjusted exponential...Ch. 12 - Describe how a forecast is monitored to detect...Ch. 12 - Explain the relationship between the use of a...Ch. 12 - Selecting from MAD, MAPD, MSE, E, and E, which...Ch. 12 - What is the difference between linear and multiple...Ch. 12 - Define the different components (y, x, a, and b)...Ch. 12 - A company that produces video equipment, including...Ch. 12 - The Hartley-Davis motorcycle dealer in the...Ch. 12 - The manager of the I-85 Carpet Outlet needs to be...Ch. 12 - The LawnPlus Fertilizer Company distributes...Ch. 12 - Graph the demand data in Problem 12.3. Can you...Ch. 12 - The chairperson of the department of management at...Ch. 12 - The manager of the Excom Service Station wants to...Ch. 12 - The Intrepid mutual fund of growth stocks has had...Ch. 12 - The Oceanside Hotel is adjacent to City Coliseum,...Ch. 12 - Mary Hernandez has invested in a stock mutual fund...Ch. 12 - Globetron manufactures components for use in small...Ch. 12 - The Bee Line Caf is well known for its popular...Ch. 12 - For the demand data in Problem 12.11, develop a...Ch. 12 - Develop a seasonally adjusted forecast for the...Ch. 12 - Backstreets Pizza delivery service has randomly...Ch. 12 - The Willow River Mining Company mines and ships...Ch. 12 - The Great Northwest Outdoor Company is a catalog...Ch. 12 - Townside Food Vending operates vending machines in...Ch. 12 - The town aquatic center has an indoor pool that...Ch. 12 - Develop an adjusted exponential smoothing forecast...Ch. 12 - During the past five months the emergency room at...Ch. 12 - At its craft store and through its website, the...Ch. 12 - A group of business students at Tech organized a...Ch. 12 - Temco Industries has developed a forecasting model...Ch. 12 - Monitor the forecast in Problem 12.23 for bias...Ch. 12 - Develop a statistical control chart for the...Ch. 12 - Monitor the adjusted exponential smoothing...Ch. 12 - Develop an exponential smoothing forecast with =...
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- 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_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_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
- 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_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
- Under what conditions might a firm use multiple forecasting methods?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_forwardAn 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_forward
- A 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_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 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_forward
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