Question 1: The SCO3001 Company is considering different tools to forecast the demand of its textbooks. Based on thefollowing data for weekly sales to answer the questions.135439a. Forecast the demand for Weeks 6 and 7 using a 3-week simple moving average (SMA) method.=b. Forecast the demand for Weeks 6 and 7 using a 4-week weighted moving average (WMA) method with w₁0.5, W₂ = 0.3, W3 = 0.15,W4 = 0.05.c.Forecast the demand for Week 3 using exponential smoothing with Ao = 33, compare the results from a = 0.1and a = 0.2, respectively. Next, evaluate which a value gives a more accurate demand forecast for the firstthree weeks if we measure forecast error byCFEWeekDemand(i)(ii)(iii)247346regular MADthe smoothed MAD (with MADo = 1)526633

Answer a= Forecast for week 6= (47+46+32+26)/4 =37.75 Forecast for week 7= (46+32+26+33)/4 =34.25…

Answered: Question 1: The SCO3001 Company is…

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_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?
Q1) Forecasting is both art and science. Describe the situations where we use Qualitative forecasting techniques and quantitative forecasting techniques
There is a bias towards under forecasting, though we cannot determine from the data provided whether that is severe bias or not O There is a severe bias towards under forecasting O Bias is negligible in this case Question 22 A MAD (Mean Absolute Deviation) of 17.3 suggests which of the following? O There is an over-forecasting bias of 17.3 O The standard deviation for the forecast data is 17.3 O All forecasts fall within 17.3 of the actual data O On average, the forecasts miss by 17.3 O Without knowing the average, the MAD is useless 0 日
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
11. Consider the following actual and forecast demand levels for Big Mac hamburgers at a local McDonald's restaurant: Day Actual Demand Forecast Demand Monday 88.00 88.00 Tuesday 72.00 88.00 Wednesday 68.00 84.00 Thursday 48.00 80.00 Friday − ? Part 2 The forecast for Monday was derived by observing Monday's demand level and setting Monday's forecast level equal to this demand level. Subsequent forecasts were derived by using exponential smoothing with a smoothing constant of 0.25. Using this exponential smoothing method, the forecast for Big Mac demand for Friday is __________ Big Macs (round your response to one decimal place).
Problem 6-01 (Algorithmic) Consider the following time series data. Week 1 2 3 4 5 6 Value 18 14 15 11 18 13 Using the naïve method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy. Mean absolute error. If required, round your answer to one decimal place.fill in the blank 1 Mean squared error. If required, round your answer to one decimal place.fill in the blank 2 Mean absolute percentage error. If required, round your intermediate calculations and final answer to two decimal places.fill in the blank 3% What is the forecast for week 7? If required, round your answer to two decimal place.fill in the blank 4
12-1. The Hartley-Davis motorcycle dealer in the Minneapolis- St. Paul area wants to be able to forecast accurately the de- mand for the Roadhog Super motorcycle during the next month. From sales records, the dealer has accumulated the data in the following table for the past year. Month January February March April Мay June Motorcycle Sales 7 10 8 7 12 July August September 10 11 12 October 10 November December 14 16 a Compute a three-month moving average forecast of demand for April through January (of the next year). b. Compore a five-month moving average forecast for June drough January. c. Compare the two forecasts computed in parts (a) and (b) using MAD. Which one should the dealer use for January of dhe next year?
Week Sales1 27502 31003 32504 28005 29006 30507 33008 31009 295010 300011 320012 3150Develop a three-week moving average. A.What is the forecast for week 5? (Make sure no decimal place and no comma. EX: 1234) B.What is the value of MSE (mean squared error) with regard to a three-week moving average? (Make sure no decimal place and no comma. EX: 1234) C.Develop the exponential smoothing with the alpha value of 0.20. What is the forecast for week 5? (Make sure no decimal place and no comma. EX: 1234) D.What is the value of MSE (mean squared error) with regard to the exponential smoothing? (Make sure no decimal place and no comma. EX: 1234) E.Which method is more accurate? (1 for Moving average, 2 for Exponential Smoothing)
c. Prepare a naive forecast for periods 2 through 11 using the given sales data. Compute each of the following; (1) MSE, (2) MAD, (3) tracking signal at month 10, and (4) 2s control limits. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) Answer is complete but not entirely correct. MSE MAD Tracking signal Control limits 0 ± I+ 28.33 X 1,221.88 x -1.25 69.91

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