Please solve in 30 minute Quarterly demands is given for the past 3 years:WinterSpringSummerFallYear 14800450041005500Year 25700380045006000Year 36000460049006500a) Develop a seasonally adjusted forecast. (Use a linear trend line model to develop a forecastestimate.)b) Forecast demands for year 4 (Winter, Spring, Summer, and Fall).c) Assess Part (a) model accuracy using MSE.Note: Develop an Excel file for doing necessary calculations.

Find the Given details below: Given details: Winter Spring Summer Fall Year 1 4800 4500…

Answered: Quarterly demands is given for the past…

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 28P: The file P13_28.xlsx contains monthly retail sales of U.S. liquor stores. a. Is seasonality present...

See similar textbooks

Similar questions

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 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 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_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_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_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?
4 Trinity General Hospital had the following number of patient admissions during the past 8 weeksWeek Patient Admissions1 1202 1453 954 1125 1306 1107 1008 140 a. Develop a 3-week weighted average forecast forWeek 4 through 9 with weightsW1 = 0.2W2 = 0.3W3 = 0.5 b. Forecast patient admissions for week 9 using simpleexponential smoothing with α = 0.2. Assume that the forecast for Week 2 (F2) is the naïve forecast.
) Consider the following time series data: Week 1 2 3 4 5 6 Value 18 13 16 11 17 14 i)Construct a time series plot. What type of pattern exist in the data? ii)Develop a three – week moving average for the time series. Compute MSE and a forecast cast for week 7. Use alpha = 0.2 to compute the exponential smoothing value for the time series. Compute MSE and a forecast for week 7. IV)Compare the three -week moving average forecast with exponential smoothing forecast using alpha = 0.2. Which appears to provide the better forecast based on MSE? Explain V)Use trial and error to find a value of the exponential smoothing. Coefficient Alpha that result in a smaller MSE than what you calculated for alpha = 0.2.
6) 4 Consider the following time series data. Month 1 2 3 4 5 6 7 Value 24 13 20 12 19 23 15 i) Construct a time series plot. What type of pattern exist in the data? ii)Develop a three – week moving average for the time series. Compute MSE and a forecast cast for week 8. iii)Use alpha = 0.2 to compute the exponential smoothing value for the time series. Compute MSE and a forecast for week 8. iv)Compare the three -week moving average forecast with exponential smoothing forecast using alpha = 0.2. Which appears to provide the better forecast based on MSE? Explain v)Use trial and error to find a value of the exponential smoothing. Coefficient Alpha that result in a smaller MSE than what you calculated for alpha = 0.2.

SEE MORE QUESTIONS

Recommended textbooks for you

Practical Management Science

Operations Management

ISBN:

9781337406659

Author:

WINSTON, Wayne L.

Publisher:

Cengage,

Contemporary Marketing

Marketing

ISBN:

9780357033777

Author:

Louis E. Boone, David L. Kurtz

Publisher:

Cengage Learning

Marketing

ISBN:

9780357033791

Author:

Pride, William M

Publisher:

South Western Educational Publishing

Practical Management Science

Operations Management

ISBN:

9781337406659

Author:

WINSTON, Wayne L.

Publisher:

Cengage,

Contemporary Marketing

Marketing

ISBN:

9780357033777

Author:

Louis E. Boone, David L. Kurtz

Publisher:

Cengage Learning

Marketing

ISBN:

9780357033791

Author:

Pride, William M

Publisher:

South Western Educational Publishing

SEE MORE TEXTBOOKS