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- Under what conditions might a firm use multiple forecasting methods?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_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 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?
- What type of analytics seeks to recognize what is going on as well as the likely forecast and make decisions to achieve the best performance possible? domain predictive prescriptive descriptive What does the robustness of a data mining method refer to? its ability to construct a prediction model efficiently given a large amount of data its speed of computation and computational costs in using the mode its ability to overcome noisy data to make somewhat accurate predictions its ability to predict the outcome of a previously unknown data set accuratelyWhich of the following methods lends itself to forecasts for long-term emerging phenomena or revolutionary technologies? A. the sales force estimate technique B. the delphi method C. the jury of executive opinion method D. market testingDiscuss the role of forecasting for the following functions of the firm:a. Marketingb. Accountingc. Financed. Production
- NING SYSTEM (ACADEMIC) Series forecasting for Business || The F-test used in testing the significance of a regression model is O a. Upper tailed test O b. Two tailed test O c. Lower tailed test O d. None OUS PAGENetflix decided to distribute the show "Orange is the New Black". This decision was because the show 'Weeds', performed very well on Netflix in terms of viewership and engagement. Both shows have the same writers/creative teams. Data shows that viewers enjoy shows by the same writers/creative teams. This is an example of Netflix relying upon which type of data analytics Question 4 options: a) Descriptive b) Diagnostic c) Predictive d) PrescriptiveA pizza chain wants to forecast the demand rate for each store for each hour in the day.What type of forecasting method is it most likely to deploy?a. Automated forecastingb. Expert panel forecastingc. Weather forecastingd. Macroeconomic forecasting