Problem 1Ralph Billett is the manager of areal estate agency. He noWwishes to develop a forecast ofthe number of houses that will besold by the agency over the nextyear. The agency's quarter-by-quarter sales figures over the lastthree years are shown below.Quarter Year 1 Year 2 Year 3130597159284532228713457652. 2. Use Average Past Forecast andfind the following forecast:a. Year 1 Quarter 3b. Year 2 Quarter 2c. Year 3 Quarter 2d. Year 3 Quarter 4(Note: Answers should be in twodecimal places)

Find the Given details below: Given details: Sales (Year) Quarter 1 2 3 1 30 59 71 2…

Answered: Ralph Billett is the manager of a real…

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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Under what conditions might a firm use multiple forecasting methods?
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.
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 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_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?
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?
Year Quarter Sales Isolated trend 2016 1 18 2016 2 28 2016 12 2016 4 8 2017 1 16 2017 2 38 2017 3 24 2017 4 17 2018 1 34 A 2018 2 40 2018 29 2018 4 26 2019 1 42 2019 2 52 2019 40 2019 4 34 43.75 2020 1 46 2020 2 58 The table contains decomposition figures of the quarterly sales in millions of Rands for a large store. The value of A is
The table below shows the sales figures for a brand of shoe over the last 12 months.Months SalesJanuary 69February 75March 86April 92May 95June 100July 108August 115September 125October 131November 140December 150a. Using the following, forecast the sales for the months up January the following yeariii. Exponential Smoothing when α= .6 and the forecast for March is 350.
A manager uses a trend equation plus quarterly relatives to predict demand. Quarter relatives areSR1 = .90, SR2 = .95, SR3 = 1.05, and SR4 = 1.10. The trend equation is: Ft= 10 + 5t. Over thepast nine quarters, demand has been as follows:Period, t 1 2 3 4 5 6 7 8 9Demand 14 20 24 31 31 37 43 48 52Is the forecast performing adequately? Explain.
Specific motors produces electronic motors for power-actuated valves for the Medicalconstruction industry. Specific's production plant has operated at near capacity forover a year now. JOSEPH Bakkabulindi, the plant manager thinks that the growth insales will continue, and he wants to develop a long- range forecast to be used to planfacility requirements for the next 3 years. Sales records for the past 10 years havebeen accumulated: N.B: Show all the workings. Solve with Linear regression. Year Annual sales (000's of units) Year Annual sales (000's of units 1 500 6 1,000 2 650 7 1,100 3 900 8 1,300 4 1,000 9 1,450 5 1,000 10 1,600
George Kyparisis owns a company that manufacturessailboats. Actual demand for George's sailboats during each ofthe past four seasons was as fo llows: George has forecasted that annual demand for his sailboatsin year 5 will equal 5,600 sailboats. Based on this data and themultiplicative seasonal model, what will the demand level be forGeorge's sailboats in the spring of year 5 ?

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