3. The weekly demand for units manufactured by the Orion Company Limited has been as follows: Week 1 Units Week Units 1000 1050 800 6 1100 3 1200 7 1250 4 1150 8 1060 Required: i. Forecast the demand for the product for week 9 by using four week weighted moving average with n-4 and weights of 0.04,0.03,0.20 and 0.10 with more recent data given more weight. Forecast the Trend by using regression method ii.

3. The weekly demand for units manufactured by the Orion Company Limited has been as follows: Week 1 Units Week Units 1000 1050 800 6 1100 3 1200 7 1250 4 1150 8 1060 Required: i. Forecast the demand for the product for week 9 by using four week weighted moving average with n-4 and weights of 0.04,0.03,0.20 and 0.10 with more recent data given more weight. Forecast the Trend by using regression method ii.

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section: Chapter Questions

Problem 40P: The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels....

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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?
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_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_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?
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.
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-Forecasting using Exponential Smoothing The first five periods of demand data are shown in the following table .Let the smoothing coefficient, alpha, equal 0.2.Compute the exponentially smoothed forecasts for periods one through four .Initialize the procedure with a forecast value for   period one of 37. Period    Aggregate Demand    Forecast demand 0                      -                                  - 1                     38                              37 2                     42 3                     40 4                     36 5                     42 Determine the Running Sum of Forecast Errors (RSFE), the Mean Absolute Deviation, MADt-1,and the Tracking Signal(TS) at the end of each period. Let the initial MADt-1   for period 0 be equal to 2.
Exercise # 2 –Calculating MADt, Revised MADt,Error,and the Revised Error :Has the Forecast Improved ? How much has it improved in % terms ? Franklin Henry forecasts demand for an auditing and accounting services group, for an estate planning group ,and a business consulting group.His group forecasts for the preceding six-month(M) period, which were made six months prior to that time, are each based on a level, patternless projection.The following data table shows these forecasts(F) and the actual demand (AD) for each group : Estate Planning                  Auditing and Accounting            Business Consulting (Client –days)                                  (Client –days)                                      (Client –days) M         AD      F                            M         AD      F                              M         AD      F 1          120    130                            1         260     230                          1       120    120 2           80     130…
Given is a historical time series for job services demand in the prior 6 months. Month Demand 1 702 2 711 3 714 4 701 5 689 6 711 The Period 6 forecast by using weighted moving average with weights of 0.05, 0.25, 0.40, and 0.30 =  (in 2 decimal places)