4. What is the difference between trend and seasonality in time series data?5. Here are the errors associated with a particular forecast over the past 5 months, inchronological order: 5, 10, -15, 0, 8. In which month was the forecast perfectlyaccurate? In which month was the forecast the least accurate? In which month ormonths was the forecast too high? (Noteshaper Ramp Up # 23)6. Tutoring Center needs to allocate tutors this week for office appointments, so it needsto forecast the number of students who will seek appointments. The director hasgathered the following time series data recently:Time PeriodCodeStudent AppointmentsJan 6 - 1095Jan 13 - 1780Jan 20 - 2465Jan 27 – 31450a) What is the naïve forecast for the number of student appointments for TimePeriod 5 (Feb 3– 7)?b) What is the 2 week moving average for Time Period 5?c) What is the 3 week moving average for Time Period 5?d) What is the forecast for Time Period 5 using exponential smoothing with alpha =0.2 and a forecast 90 for Time Period 3?e) Is demand for appointments increasing or decreasing?

Note: - Since the exact question that has to be answered is not specified, we will answer only the…

Answered: 4. What is the difference between trend…

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 25P: The file P13_25.xlsx contains the quarterly numbers of applications for home mortgage loans at a...

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Under what conditions might a firm use multiple forecasting methods?
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_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_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_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_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 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?
The file P13_25.xlsx contains the quarterly numbers of applications for home mortgage loans at a branch office of Northern Central Bank. 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? Is it guaranteed to produce better forecasts for the future?
4 .Substitute for Problem # 4 on in attachment)-Calculate the MAD,revised MAD and the Revised Forecast for months 1-6 for the following three groups,where the Forecast stands for Forecast Demand.Has your forecast improved ? Tell me how would you go about improving the revised forecast( for each of the three groups)without actually doing so? Estate Planning Group Auditing and Accounting Group Business Consulting Group Month Actual Demand Forecast Actual Demand Forecast Actual Demand Forecast 1 100 125 270 230 140 135 2 90 125 240 230 130 135 3 110 125 280 230 160 135 4 115 125 260 230 180 135 5 130 125 300 230 200 135 6 115 125 220 230 190 135
Accuracy of forecasts. The manager of a large manufacturer of industrial pumps must choose betweentwo alternative forecasting techniques. Both techniques have been used to prepare forecasts for a sixmonth period. Using MAD as a criterion, which technique has the better performance record?FORECASTMonth Demand Technique 1 Technique 21 492 488 4952 470 484 4823 485 480 4784 493 490 4885 498 497 4926 492 493 493

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