Given the following original forecast and actual data:Month ActualForecastJan600Feb475Mar550Apr575May643Jun662Jul??????Using a 2 month weighted moving average with weights of 75% for theprevious month and 25% for two months back, what is the forecast for July.Round your answer to 1 decimal place.400--————

Weighted moving average forecasting -Ft= W1 × At-1 + W2 × At-2W1+W2Where-Ft = Forecast value of…

Answered: Given the following original forecast…

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 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 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_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 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?
Given the data... Month Actual Jan 600 Feb 475 Mar 550 Apr 575 May 688 Jun 649 Jul ??? Forecast 400 —— -- ??? Calculate the MAD (Mean Absolute Deviation) for March - June for your exponential smoothing forecast with a = 0.64. Round your answer to 2 decimal places.
Given the following original forecast and actual data: Month Actual Forecast Jan Feb Mar Apr May Jun Jul 600 475 550 575 688 649 ??? 400 —— 1 ??? Using exponential smoothing with a - = 0.64, what is the forecast for July. Round your answer to 2 decimal places.
Given the following original forecast and actual data: Month Actual Forecast Jan 600 Feb 475 Mar 550 Apr 575 May 670 Jun 652 Jul ??? Using a 2 month weighted moving average with weights of 80% for the previous month and 20% for two months back, what is the forecast for July. Round your answer to 1 decimal place. Answer: ??? 400 ——
A Use a simple moving average model. Experiment with the models using five weeks and three weeks past data. 3 week MA Week ATL BOS CHI DAL LA TOTAL 1 2 3 4 5 6 7 8 9 10 11 12 13 5 week MA Week ATL BOS CHI DAL LA TOTAL 1 2 3 4 5 6 7 8 9 10 11 12 13 B evaluate the forecast that would have been made over the 13 weeks using overall mean absolutely deviation, mean absolute percent error, and tracking signal as criteria.
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
7. Using both time series methods of forecasting, predict the number of meals that should be prepared for the next Monday. The forecast for the most recent Monday was 367 and for the most recent Friday was 354. a=0.3 Show your calculations DAY Monday Tuesday Wednesday Thursday Friday WEEK 1 360 372 375 358 333 WEEK 2 375 381 373 368 377 WEEK 3 367 376 378 360 353 WEEK 4 373 380 376 363 370

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