Obtain the linear trend equation for the following data on new checking accounts at Fair Savings Bank and use it to predict expected new checking accounts for periods 16 through 19.Period New Accounts Period New Accounts Period New Accounts1 200 6 232 11 2812 214 7 248 12 2753 211 8 250 13 2804 228 9 253 14 2885 235 10 267 15 310b. Use trend-adjusted smoothing with α = .3 and β = .2 to smooth the new account data in part a.What is the forecast for period 16? 9. a. Obtain the linear trend equation for the following data on new checking accounts at Fair SavingsBank and use it to predict expected new checking accounts for periods 16 through 19.PeriodNew AccountsPeriodNew AccountsPeriodNew Accounts12006232112812214724812275321182501328042289253142882351026715310b. Use trend-adjusted smoothing with a = .3 and ß = .2 to smooth the new account data in part a.What is the forecast for period 16?

Given details: Given details: Period New Accounts 1 200 2 214 3 211 4 228 5 235 6…

Obtain the linear trend equation for the following data on new checking accounts at Fair Savings Bank and use it to predict expected new checking accounts for periods 16 through 19. Period New Accounts Period New Accounts Period New Accounts 1 200 6 232 11 281 2 214 7 248 12 275 3 211 8 250 13 280 4 228 9 253 14 288 5 235 10 267 15 310 b. Use trend-adjusted smoothing with α = .3 and β = .2 to smooth the new account data in part a. What is the forecast for period

Obtain the linear trend equation for the following data on new checking accounts at Fair Savings Bank and use it to predict expected new checking accounts for periods 16 through 19. Period New Accounts Period New Accounts Period New Accounts 1 200 6 232 11 281 2 214 7 248 12 275 3 211 8 250 13 280 4 228 9 253 14 288 5 235 10 267 15 310 b. Use trend-adjusted smoothing with α = .3 and β = .2 to smooth the new account data in part a. What is the forecast for period

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.7: Exponential Smoothing Models

Problem 29P: The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building...

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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_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 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?
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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?
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