Problem 3-31 (Algo)The following are sales revenues for a large utility company for years 1 through 11. Forecast revenue for years 12 through 15. Becausewe are forecasting four years into the future, you will need to use linear regression as your forecasting method. (Enter your answers Inmlons.)YEARREVENJE (MILLIONS)$4,875.05,865.75,523.45,729.95,500.95,190.0$5,097.25,112.35,553.85,734.45,863.5121011PeriodForecast12131415

Year ( X ) REVENUE ( Y ) XY X2 1 4875.0 4875 1 2 5065.7 10131.4 4 3 5523.4 16570.2 9…

Answered: The following are sales revenues for a…

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section: Chapter Questions

Problem 42P: The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars)...

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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?
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.
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.
The 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?
The 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?
The 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?
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?
The 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?
The following table represents sales data for milk (in hundred liters) sold by a grocery.Do the computations to fill out the table and answer the following questions:1. Using MAD as the criterion, which of the following models would you use for thegiven time series data? Why?A. Naïve approach;B. 5-month SMA model;C. WMA model with weights 0.1, 0.3, and 0.6; orD. ES model with α = 0.5 and a forecast of 3,500 liters in the first month.NOTE: In answering Item 1, mention the whole description of the model; i.e., not just“SMA model”, but “SMA model with n = ...”; not just “WMA model”, but “WMA modelwith weights ...”; not just “ES model”, but “ES model with α = ...”.2. Interpret the MAD of the most accurate among the forecasting models above.3. Based on your decision in Item 1, what should be the forecast for Month 11?
Café Michigan's manager, Gary Stark, suspects that demand for mocha latte coffees depends on the price being charged. Based on historical observations, Gary has gathered the following data, which show the numbers of these coffees sold over six different price values: Price Number Sold $2.70 760 $3.40 515 $2.10 990 $4.10 250 $3.20 325 $4.10 475 Using simple linear regression LOADING... and given that the price per cup is $1.80, the forecasted demand for mocha latte coffees will be nothing cups (enter your response rounded to one decimal place).
a) The demand forecast for Month 6 would be: A. 565 haircuts B. 574 haircuts C. 578 haircuts D. 584 haircuts b) With Mean Absolute Deviation (MAD) as the criterion, the best forecasting model for this time series data is: A. Naïve approach B. 2-week Simple Moving Average (SMA) C. Weighted Moving Average (WMA) with weights: 0.5, 0.3, 0.2 D. Exponential Smoothing (ES) with alpha = 0.8
Read the following passage and answer the question that follows Quincy Snodgrass Enterprises—Forecasting Quincy Snodgrass is an entrepreneur and a lover of the outdoors. He has worked for various companies since he graduated college with his business administration degree in management. Over the years, he has saved every extra penny and now has the starting capital he needs; consequently, he plans to open his own business. Quincy plans to open a landscaping business. The primary services he’ll offer are grass cutting, edging, and bush trimming. Obviously, this will only provide income in the spring, summer, and early fall. Therefore, he plans to offer snow removal in the winter. His goal is to continue to provide those baseline services and expand into actual landscaping work. Quincy’s initial challenge is to develop a forecast of how many customers he’ll have each month. This is essential to determine if he needs to hire any additional labor throughout the season. Unfortunately, none…

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