The monthly sales for Yazici Batteries, Inc., were as follows:
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- Under what conditions might a firm use multiple forecasting methods?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 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.Sales of hair dryers at the Walgreens stores in Youngstown,Ohio, over the past 4 months have been 100, 110, 120, and 130units (with 130 being the most recent sales).Develop a moving-average forecast for next month, usingthese three techniques:a) 3-month moving average.b) 4-month moving average.c) Weighted 4-month moving average with the most recentmonth weighted 4, the preceding month 3, then 2, and theoldest month weighted 1.d) If next month’s sales turn out to be 140 units, forecast the following month’s sales (months) using a 4-month mov-ing average.The monthly sales for Yazici Batteries, Inc., wereas follows: a) Plot the monthly sales data.b) Forecast January sales using each of the following:i) Naive method.ii) A 3-month moving average.iii) A 6-month weighted average using .1, .1, .1, .2, .2, and.3, with the heaviest weights applied to the most recentmonths.iv) Exponential smoothing using an a = .3 and aSeptember forecast of 18.v) A trend projection.c) With the data given, which method would allow you toforecast next March’s sales?
- The monthly sales for Telco Batteries, Inc. are as follows (see image): Plot the monthly sales data Forecast January sales using each of the following: Naïve method A 3-month moving average A 6-month weighted average using 0.1, 0.1, 0.1, 0.2, 0.2, and 0.3 with the heaviest weights applied to the most recent month Exponential smoothing using an alpha = 0.3 and a September forecast of 18 A trend projection With the data given, which method would allow you to forecast next March’s sales?Sales of hair dryers at the Walgreens stores in Youngstown, Ohio, over the past 4 months have been 100, 110, 120, and 130 units (with 130 being the most recent sales). Develop a moving-average forecast for next month, using these three techniques:a) 3-month moving average.b) 4-month moving average.c) Weighted 4-month moving average with the most recent month weighted 4, the preceding month 3, then 2, and the oldest month weighted 1.d) If next month’s sales turn out to be 140 units, forecast the following month’s sales (months) using a 4-month moving average.Consider the following sales data for Bell, Inc. Month Sales ($ Millions) Jan. 10 Feb. 12 March 14 April 16 May 18 June 23 July 26 Aug. 31 Sept. 27 Oct. 18 Nov. 16 Dec. 14 a. Use a three-month weighted moving average to forecast the sales for the months April through December. Use weights of (3/6), (2/6), and (1/6), giving more weight to more recent data. b. Use exponential smoothing with α = 0.30 to forecast the sales for the months April through December. The forecast for January was $12 million. c. Compare the performance of the two methods by using the bias and MAD as the performance criteria. Which performance method is the most reasonable? Why?
- Choose the type of forecasting technique (survey, Delphi, averaging, seasonal, naive, trend, orassociative) that would be most appropriate for predicting:b. Popularity of a new television series.Hair dryers have sold 100, 110, 120, and 130 units at Walgreens stores in Youngstown, Ohio, during the last four months ( with 130 being the mostrecent sales).Using these three strategies, build a moving-average forecast for next month:a) The three-month moving averageb) Moving average every four months.c) Weighted 4-month moving average, with the most recent month weighted4, the month before 3, then 2, and the month before that weighted 1, and the month before that weighted 1.d) Using a 4-month rolling average, predict the next month's sales (months) ifnext month's sales are 140 units.Choose the type of forecasting technique (survey, Delphi, averaging, seasonal, naive, trend, orassociative) that would be most appropriate for predicting:d. The impact a price increase of 10 percent would have on sales of orange marmalade.
