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

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

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.6: Moving Averages Models

Problem 22P: The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six...

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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_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 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_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?
It wouldnt let me take a screenshot of the table so I had to copy and paste it in here. My question is: How do you Calculate a moving average forecast using the last 2 and 4 dates of stay. What is the prediction for 7/1/2021 for each technique? Discussion Question 1 - 200 room hotel Forecasts MA 2 Errors MA 4 Errors ES (0.8) Errors ES (0.5) Errors Date Day of Week Rooms Sold MA 2 MA 4 ES (0.8) ES (0.5) AE APE AE APE AE APE AE APE 6/1/2021 Tuesday 123 6/2/2021 Wednesday 109 6/3/2021 Thursday 140 6/4/2021 Friday 199 6/5/2021 Saturday 179 6/6/2021 Sunday 140 6/7/2021 Monday 117 6/8/2021 Tuesday 132 6/9/2021 Wednesday 108 6/10/2021 Thursday 151…
The following gives the number of pints of type B blood used at Woodlawn Hospital in the past 6 weeks: Week Of Pints Used August 31 360 September 7 389 September 14 410 September 21 381 September 28 368 October 5 374 a. Forecast the demand for the week of October 12 using a 4-week moving average. [Select] b. What is the MAD? [Select] c. Use a 4-week weighted moving average, with weights of .1, .3, .6, and .8 using .8 for the most recent week. Forecast demand for the week of October 12. [Select] d. What is the MAD? [Select] e. Compute the forecast for the week of October 12 using exponential smoothing with a forecast for August 31 of 363 and a = .47 [Select] f. What is the MAD? [Select] g. Based on evaluating all three forecasting models, which forecast should be chosen? [Select]
Accuracy of forecasts. The manager of a large manufacturer of industrial pumps must choose betweentwo alternative forecasting techniques. Both techniques have been used to prepare forecasts for a sixmonth period. Using MAD as a criterion, which technique has the better performance record?FORECASTMonth Demand Technique 1 Technique 21 492 488 4952 470 484 4823 485 480 4784 493 490 4885 498 497 4926 492 493 493
Here are the data for the past 21 months for actual sales of a particular product: LAST YEAR THIS YEAR January 315 February 418 March 390 April 430 May 400 [ June 480 July 430 August 320 September 385 October 530 November 592 555 December 300 400 350 460 390 395 355 245 340 Develop a forecast for the fourth quarter using a three-quarter, weighted moving average. Weight the most recent quarter 0.50, the second most recent 0.25, and the third 0.25. Do the problem using quarters, as opposed to forecasting separate months. (Round your answer to 2 decimal places.) Forecast for the fourth quarter