The consulting agency begins collecting data on actual population demandutilization. The first year s data is:PeriodMarch 2020April 2020May 2020June 2020Demand31,55040,12538,24335,31132,20732,12535,70040,620nPeriodDemandSeptember 41,4822020October 2020November2020December2020January 202138,450February 2021 33,33038,34834,72236,190July 202042,810August 202044,230What is the forecast demand for March 2021 considering trend and seasonality?

To be determined: The consulting agency begins collecting data on actual population utilization, so…

Answered: The consulting agency begins collecting…

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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Similar questions

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?
ces Obtain estimates of quarter relatives for these data using the centered moving average method. ( decimal points and standardize the final seasonal relatives so their total is four.) Quarter Demand Year 1 2 3 4 1 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 16 20 35 49 31 36 65 74 50 52 92 86 61 Quarter 1 2 3 4 1 2 3 4 1 2 3 4 1 Year MA 2 MA₂ Demand/MA2
3. Quarterly sales of a car dealer exhibit an almost constant mean over time, but sales fluctuate depending on the quarter of the year (exhibits seasonality). Quarter Q1 - Spring Q2 - Summer Q3 - Fall Q4 - Winter Mean Year 1 32 74 53 22 45.25 Year 2 30 71 63 15 44.75 Year 3 35 83 45 19 45.5 (a) Using three years' worth of quarterly data that is provided, develop a set of seasonal factors that could be used to forecast car sales (b) Forecast car sales in each of the four quarters (Q1-Q4) of the following year (Year 4), using these seasonal factors.
Calculate a regression line for the data and indicate the forecast of sales for a store with advertising spending of R240,000? and R300,000? Store Advertising Sales 1 14 6 2 11 3 3 15 5 4 16 5 5 24 15 6 28 18 7 22 17 8 21 12 9 26 15 10 43 20 11 34 14 12 9 5
The owner of Leisure Boutique in a local mall is reviewing their forecasting model for errors. Month Actual Forecasted March 64 63 April 66 67 May 70 71 June 78 75 July 80 79 August 84 83 September 92 87 October 94 91 What is the cumulative forecast error? Group of answer choices a) -16 b) 16 c) 12 d) -12
The monthly demand for units manufactured by the Acme Rocket Company has been as follows:Month Units Month UnitsMay 100 September 105June 80 October 110July 110 November 125August 115 December 120a. Use the exponential smoothing method to forecast the number of units for June to January. Theinitial forecast for May was 105 units; a = 0.2.b. Calculate the absolute percentage error for each month from June through December and theMAD and MAPE of forecast error as of the end of December.c. Calculate the tracking signal as of the end of December. What can you say about the performanceof your forecasting method?
The following table shows the three-period moving average and five-period moving average for monthly sales of Budget Furniture's during 2019. Moving averages of Budget Furniture's Time period Months Sales Three-period moving average (rounded off to Five-period moving average four decimals) R'millions 1 Jan 7 5.0000 6.2 February 5.6667 6.6 March 5 7.0000 B 4 April 8.3333 8.2 May 7 9.3333 8.4 June 8.3333 9.6 7 July 12 8.6667 9.6 August 4 A 9.2 September 10 10.6667 10 October 13 10.6667 11 November 9 12 December 10 The seasonal index for the month of February in 2019 is: LO
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
es Compute seasonal relatives for this data using the simple averaging (SA) method: Quarter Year 1 1 AWN 2 3 4 Quarter 1 Quarter 2 Quarter 3 Quarter 4 W # 3 2 (Round all your answers to three decimal points.) FB 5 1 4 Year 2 1 4 1 Q Search F4 S4 R F5 % 45 Year 3 0 8 4 6 ‒‒‒‒‒‒ 8 F9 F10 F11 0

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