Chapter 11, Problem 3.1C

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_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 file P13_27.xlsx contains yearly data on the proportion of Americans under the age of 18 living below the poverty level. 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. Create a chart of the series with the forecasts superimposed from this optimal smoothing constant. Does it make much of an improvement over the model in part b? d. Write a short report to summarize your results. Considering the chart in part c, would you say the forecasts are good?

Below is a table containing data on product demand for the most recent five months along with the forecasts that had been made for those 5 previous months. Use the 3-period simple moving average method to forecast the number of sales to expect next month. Month Demand Forecast 1 308 341 388 343.3 3 344 364.7 4 400 346.7 341 377.3

Below is a table containing data on product demand for the most recent five months along with the forecasts that had been made for those 5 previous months. Use the 3-period weighted moving average method to forecast the number of sales to expect next month.  Use the following weights:  0.4 for the most recent, 0.3 for the 2nd most recent, and 0.3 for the 3rd most recent. Month Demand Forecast 1 308 337 2 388 341.4 3 344 368 4 400 342.8 5 341 378.4

B. The following table shows the number of televisions sold over the last ten years at a local electronic store. year TV sales 1 150 2 300 3 480 4 600 5 630 6 640 7 700 8 825 9 900 10 980 i. Using the trend projection, develop a formula to predict sales for years 11 and 12. Develop a table to calculate the slope and intercept.    Please Show All Working. Use that formula to forecast television sales for years 11 and 12

PLEASE CHOOSE ONE ANSWER AND CLARIFY THE CHOICE A quantitative forecasting class assumes that sales (or other items to be forecast) follow a repetitive pattern over time. When a retailer uses daily sales of each product to identify patterns and to forecast inventory requirements, this is an example of: A::a deterministic model B::a causal model C::a time series forecasting technique D::a qualitative model       “A” items are high-dollar value items which represent a small portion (usually 10-20 percent) of requisitions, purchase orders, and inventory items, but a large portion of annual spend (usually 70-80 percent). “A” items in ABC analysis are: A::reviewed infrequently B::normally carried in large quantities C::stored in a relatively insecure warehouse D::particularly critical in financial terms       Decoupling inventories are carried __________________________________. The amounts and locations of raw material, work-in-process, and finished goods decoupling inventories depend on…

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