(Use this excel file to show formulas used) 1. Tom Simpson, Director of the Chamber of Commerce for Exeter township is investigating the past ten years oftourist visits to the area. The following data has been gathered on number of tourists who signed into the localinformation center.YearNumber of tourists17002.24836334581410158816291301145519896-10Tom is interested in implementing a forecasting system and is investigating the following forecasting methods aspossibilities:Simple moving average, Weighted moving average, Simple Exponential Smoothing, Regression.Choose two of the above methods and calculate forecasts for Year 11If you use Weighted moving average, use .5, .3, .2 for the weights (.5 for most recent and so on)If you use Exponential Smoothing, begin with the assumption the Forecast for Year 9 was 1500Use .3 for your alpha if you use this method.Present your forecast results9°Flostly cloudy[田DELL456 78 AВCEGH.KM1Forecast 13.YearNumber of TouristsSimple MovingError|Error|Error|Error|%WeightsAlphaIntercept17000.20.3238.42480.3Slope36330.5164.1374458814109.6.15881071629118.1301121455In Green Cells, write the name of the forecast type:Simple moving average, Weighted moving average,Simple Exponential Smoothing, Regression13101989141115MADMSEМАРЕ16P.2.2.4.

Forecasting is a technique used to predict future outcomes on the basis of past data. In businesses…

1. Tom Simpson, Director of the Chamber of Commerce for Exeter township is investigating the past ten years of tourist visits to the area. The following data has been gathered on number of tourists who signed into the local information center. Year Number of tourists 1 12345 6 7 8 9 10 ● 700 248 633 458 1410 1588 1629 Tom is interested in implementing a forecasting system and is investigating the following forecasting methods as possibilities: ● 1301 1455 1989 Simple moving average, Weighted moving average, Simple Exponential Smoothing, Regression. Choose two of the above methods and calculate forecasts for Year 11 o If you use Weighted moving average, use .5, .3, .2 for the weights (.5 for most recent and so on) o If you use Exponential Smoothing, begin with the assumption the Forecast for Year 9 was 1500 Use .3 for your alpha if you use this method. Present your forecast results .

1. Tom Simpson, Director of the Chamber of Commerce for Exeter township is investigating the past ten years of tourist visits to the area. The following data has been gathered on number of tourists who signed into the local information center. Year Number of tourists 1 12345 6 7 8 9 10 ● 700 248 633 458 1410 1588 1629 Tom is interested in implementing a forecasting system and is investigating the following forecasting methods as possibilities: ● 1301 1455 1989 Simple moving average, Weighted moving average, Simple Exponential Smoothing, Regression. Choose two of the above methods and calculate forecasts for Year 11 o If you use Weighted moving average, use .5, .3, .2 for the weights (.5 for most recent and so on) o If you use Exponential Smoothing, begin with the assumption the Forecast for Year 9 was 1500 Use .3 for your alpha if you use this method. Present your forecast results .

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.6: Moving Averages Models

Problem 20P: The file P13_20.xlsx contains the monthly sales of iPod cases at an electronics store for a two-year...

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The file P13_02.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the companys suspicion. b. By what percentage are sales increasing each month? c. What is the MAPE for the forecast model in part b? In words, what does it measure? Considering its magnitude, does the model seem to be doing a good job? d. In words, how does the model make forecasts for future months? Specifically, given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months?
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_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_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?
Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semifragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P13_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semifragile cargo.
The file P13_19.xlsx contains the weekly sales of a particular brand of paper towels at a supermarket for a one-year period. a. Using a span of 3, forecast the sales of this product for the next 10 weeks with the moving averages method. How well does this method with span 3 forecast the known observations in this series? b. Repeat part a with a span of 10. c. Which of these two spans appears to be more appropriate? Justify your choice.
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?
A small computer chip manufacturer wants to forecast monthly ozperating costs as a function of the number of units produced during a month. The company has collected the 16 months of data in the file P13_34.xlsx. a. Determine an equation that can be used to predict monthly production costs from units produced. Are there any outliers? b. How could the regression line obtained in part a be used to determine whether the company was efficient or inefficient during any particular month?
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?
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?