Please complete in Excel in forecasting…Question 2: Suppose that you work for a U.S. senator who is contemplating writing bill that would put a national sales tax in place. Because the tax would be levied on the sales revenue of retail stores, the senator has asked you to prepare a forecast of retail store sales for year 8, based on data from year 1 through 7. The data are:Year Retail Store Sales1 $1,2252 1,2853 1,3594 1,3925 1,4436 1,4747 1,467a.Use the first naive forecasting model presented in the chapter to prepare a forecast of retail store sales for each year from 2 through 8.b.Prepare a time-series graph of the actual and forecast values of retail store sales for the entire period. (You will not have a forecast for year 1 or an actual value for year 8).c.Calculate the root-mean-squared error for your forecast series using the values for year 2 through 7.Question 3: Use the naive forecasting model presented in this chapter to answer parts (a)through (c)of Exercise 2. Use P= 0.2 in preparing the forecast. Which model do you think works the best? Explain why.Question 5: Go to the library and look up annual data for population in the United States from 1981 through 2004. One good source for such data is the Economic Report of the President, published each year by the U.S. Government Printing Office. This series is also available at a number of Internet sites, includinghttp://www.economagic.com.Plot the actual data along with the forecast you would get by using the first naive model discussed in this chapter. (Same method as used in steps (a) and (b) of Question 2).Question 8: As the world economy becomes increasingly interdependent, various exchange rates between currencies have become important in making business decisions. For many U.S. businesses, the Japanese exchange rate (in yen per U.S. dollar) is an important decision variable. This exchange rate (EXRJ) is shown in the following table by month for a two-year period:PeriodEXRJY1 M1127.36Y1 M2127.74Y1 M3130.55Y1 M4132.04Y1 M5137.86Y1 M6143.98Y1 M7140.42Y1 M8141.49Y1 M9145.07Y1 M10142.21Y1 M11143.53Y1 M12143.69Y2 M1144.98Y2 M2145.69Y2 M3153.31Y2 M4158.46Y2 M5154.04Y2 M6153.7Y2 M7149.04Y2 M8147.46Y2 M9138.44Y2 M10129.59Y2 M11129.22Y2 M12133.89Prepare a time-series plot of this series, and use the naive forecasting model to forecast EXRJ for each month from year 1 M2 (February) through year 3 M1 (January). Calculate the RMSE for the period from Y1 M2 through Y2 M12.
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