BUSI 405 Weekly Exercise 3 solutions complete answers
4. The smoothing constant chosen in simple exponential smoothing determines the weight to be placed on different terms of time-series data. If the smoothing factor is high rather than low, is more or less weight placed on recent observations? If is .3, what weight is applied to the observation four periods ago?
6. The following inventory pattern has been observed in the Zahm Corporation over 12 months:
Use both three-month and five-month moving-average models to forecast the inventory for the next January. Use mean absolute percentage error (MAPE) to evaluate these two forecasts.
11. a. Plot the data presented in Exercise 7 to examine the possible existence of trend and seasonality in the data.
b. Prepare three separate exponential smoothing models to forecast the full-service restaurant sales data using the monthly data.
1. A simple smoothing model
2. Holt’s model
3. Winter’s model
c. Examine the accuracy of each model by calculating the mean absolute percentage error for each during the historical period. Explain carefully what characteristics of the original data led one of these models to have the lowest MAPE.
12. The data in the table below represent warehouse club and superstore sales in the eastern and central United States on a monthly basis. The data are in millions of dollars.
a. Prepare a time-series plot of the data, and visually inspect that plot to determine the characteristics you see in this series.
b. Use an exponential smoothing model to develop a forecast of sales for the next 12 months, and explain why you selected that model. Plot the actual and forecast values. Determine the MAPE for your model during the historical period.
13. The data in the table below are for retail sales in book stores by quarter.
a. Plot these data and examine the plot. Does this view of the data suggest a particular smoothing model? Do the data appears to be seasonal? Explain.
b. Use an exponential smoothing method to forecast the next four quarters. Plot the actual and forecast values.
16. How are simple moving averages models different from exponential smoothing models?
