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 with the heaviest weights applied to the most recent montE iv) Exponential smoothing using an a = .3 and a Septemt forecast of 18.
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- 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?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 file P13_28.xlsx contains monthly retail sales of U.S. liquor stores. 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 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?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?
- 9 10 38 36 39 Month 1 2 4 5 7 11 12 Flat-Screen Sales 30 32 30 39 33 34 34 30 36 a) Determine the one-step-ahead flat-screen sales forecasts for the first month of next year using 3- and 5-month moving averages. b) Using a 5-month moving average, determine the one-step-ahead flat-screen forecasts for the 7th through 12th months. Compute the MAD. c) Suppose that exponential smoothing is used with a smoothing constant alpha = 0.1 to forecast flat-screen sales for the 7th through 12th months. d) Based on MAD which method did better?1. Discuss the differences between Qualitative and Quantitative forecasting models. How do Associative and Time Series techniques differ? 2. What is the Mean Absolute Deviation (MAD)? 3. Use the following set of data to calculate the Mean Absolute Deviation (MAD) for the following set of data. Actual (A:) Forecast (Ft) Forecast Error Absolute (Deviation) Month Forecast Eror January February March 45 45 42 50 34 45 April 48 40 Мay 38 45 MAD =4.4 A check-processing center uses exponential smooth- ing to forecast the number of incoming checks each month. The number of checks received in June was 40 million, while the fore- cast was 42 million. A smoothing constant of .2 is used. a) What is the forecast for July? b) If the center received 45 million checks in July, what would be the forecast for August? c) Why might this be an inappropriate forecasting method for this situation? Px .. 4.5 The Carbondale Hospital is considering the purchase of a new ambulance. The decision will rest partly on the antici- pated mileage to be driven next year. The miles driven during the past 5 years are as follows: YEAR 1 2 3 4 5 a) Forecast the mileage for next year (6th year) using a 2-year moving average. b) Find the MAD based on the 2-year moving average. (Hint: You will have only 3 years of matched data.) c) Use a weighted 2-year moving average with weights of 4 and .6 to forecast next year's mileage. (The weight of .6 is for the most…