2. Suppose that the University of the West had the following data of its growth of enrollment from2016-2014.YearEnrollment200630002007320020083600200936502010400020114200201243002013441020144520a. Forecast the 2015 enrollment using three-year weighted moving average forecast.b. Using smooth a = 0.30, forecast the 2015 enrollment.c. Using MAD, solve for the forecasting error at a = 0.10d. Use four-year weighted moving average from 2006-2015

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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 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?
3. A large Portland manufacturing would like to forecast the monthly demand for a piece of pollutioncontrol equipment. Suppose the monthly sales figures for the past five months have been as follows:Month 1 2 3 4 5Actual Demand 14 17 20 21 25a. Make a forecast for 6th month using a three-period moving average (MA). What would be the forecastfor 6th month?b. The manager would like to use simple exponential smoothing to forecast. Use alpha = 0.25. Whatwould be the forecast for 6th month?c. Find the better forecast out of Moving Average and Simple Exponential Smoothing
6) 4 Consider the following time series data. Month 1 2 3 4 5 6 7 Value 24 13 20 12 19 23 15 i) Construct a time series plot. What type of pattern exist in the data? ii)Develop a three – week moving average for the time series. Compute MSE and a forecast cast for week 8. iii)Use alpha = 0.2 to compute the exponential smoothing value for the time series. Compute MSE and a forecast for week 8. iv)Compare the three -week moving average forecast with exponential smoothing forecast using alpha = 0.2. Which appears to provide the better forecast based on MSE? Explain v)Use trial and error to find a value of the exponential smoothing. Coefficient Alpha that result in a smaller MSE than what you calculated for alpha = 0.2.
Corporate triple-A bond interest rates for 12 consecutive months follow. Month 1 2 3 4 5 6 7 8 9 10 11 12 Value 9.5 9.3 9.4 9.6 9.8 9.7 9.8 10.5 9.9 9.7 9.6 9.6 a. Construct a time series plot. What type of pattern exists in the data? b. Develop the three-month moving averages for this time series. Compute MSE. c. Compute MAE. d. Compute MAPE. e. What is the three-month moving average forecast for the next month (month 13)?
4 .Substitute for Problem # 4 on in attachment)-Calculate the MAD,revised MAD and the Revised Forecast for months 1-6 for the following three groups,where the Forecast stands for Forecast Demand.Has your forecast improved ? Tell me how would you go about improving the revised forecast( for each of the three groups)without actually doing so? Estate Planning Group Auditing and Accounting Group Business Consulting Group Month Actual Demand Forecast Actual Demand Forecast Actual Demand Forecast 1 100 125 270 230 140 135 2 90 125 240 230 130 135 3 110 125 280 230 160 135 4 115 125 260 230 180 135 5 130 125 300 230 200 135 6 115 125 220 230 190 135
The table shows the values found in the error analysis. What method of forecasting would be best to use? What is the forecasted value? MSE forecast for week 13 Time Series 1.08 14.76 3-week MA 3.04 13.67 exp smoothing 11.04 9.5 Group of answer choices A.) Time Series, forecasted value = 14.76 B.) 3-week MA, forecasted value = 13.67 C.) exp smoothing, forecasted value = 9.5 D.) Time Series, forecasted value = 1.08 E.) 3-week MA, forecasted value = 3.04 F.) exp smoothing, forecasted value = 11.04
4 Trinity General Hospital had the following number of patient admissions during the past 8 weeksWeek Patient Admissions1 1202 1453 954 1125 1306 1107 1008 140 a. Develop a 3-week weighted average forecast forWeek 4 through 9 with weightsW1 = 0.2W2 = 0.3W3 = 0.5 b. Forecast patient admissions for week 9 using simpleexponential smoothing with α = 0.2. Assume that the forecast for Week 2 (F2) is the naïve forecast.
Winter’s method is being used to forecast quarterlyU.S. retail sales (in billions of dollars). At the end of the firstquarter, Lt 300, Tt 30, and the seasonal indexes are asfollows: quarter 1, 0.90; quarter 2, 0.95; quarter 3, 0.95;quarter 4, 1.20. During the second quarter, retail sales are$360 billion. Assume a 0.2, b 0.4, and g 0.5.a At the end of the second quarter, develop a forecastfor retail sales during the fourth quarter of the year.b At the end of the second quarter, develop a forecastfor the second quarter of the following year.
1. Consider the total production (and sales) of ice cream in Canada (in millions of liters) for the period 1995 until 2007 (from left to right): 341, 331, 317, 315, 321, 278, 298, 311, 302, 302, 335, 320, 285Fit a model to ice cream production data using each of the following techniques and forecast the 2008 production in each case. Also, plot the two moving average forecasts and the actual, the two exponential smoothing forecasts and the actual, and the linear trend and the actual (three graphs altogether). Two-year moving average. Four-year moving average. Exponential smoothing with smoothing constant = 0.2. Exponential smoothing with smoothing constant = 0.4. Linear trend (regression). f. Just by observing the plots, which of the above techniques would you use to forecast the ice cream production and why? (Hint: The plot overall closest to actual demand will be most accurate).g. Alternatively,computetheMADforeachforecastingtechniqueanddetermine the most accurate…

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