The Costello Music company has been in business for 5 years. During that time its sales of electric organs have grown from 12 units to 74 units per year. Fred Costello, the firm’s owner wants to forecast next year’s organ sales. The historical data follows: Year 1 2 3 4 5 Sales 12 28 34 50 74 a) What forecasting method do you recommend and why? b) Use your recommendation to obtain the forecast for Years 6 and 7.

The Costello Music company has been in business for 5 years. During that time its sales of electric organs have grown from 12 units to 74 units per year. Fred Costello, the firm’s owner wants to forecast next year’s organ sales. The historical data follows: Year 1 2 3 4 5 Sales 12 28 34 50 74 a) What forecasting method do you recommend and why? b) Use your recommendation to obtain the forecast for Years 6 and 7.

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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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 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_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_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_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 Grand Bakery produces 60 special sourdough rolls every day. Any rolls that are not sold each day are given to the employees. They have collected sales data from the past week: Day Rolls sold 1 50 2 50 3 48 4 60 5 53 6 60 What is the value of F6 if they use a 3-day weighted moving average with W1 = 0.6, W2 = 0.2, and W3 = 0.2? Using the data from above, calculate the forecast for period 7 using a four-period moving average:
The number of fishing rods selling each day is given below. Perform analyses of the time series to determine which model should be used for forecasting. 3 day moving average analysis 4 day moving average analysis 3 day weighted moving average analysis with weights W1=0.2, W2=0.3 and W3=0.5 with W1 on the oldest data. Exponential smoothing analysis with A=0.3 Which model provides a better fit of the data? Forecast day 13 sales of fishing rods using the model chosen in part (e) Day Rods Sold 1 60 2 70 3 110 4 80 5 70 6 85 7 115 8 105 9 65 10 75 11 95 12 85 Please read the relevant article, found in the VLE, before answering the question. Discuss the process and findings of the study of the article. Suggest a possible study that could be done at your current or past job that could use a similar methodology and analysis.
An electronic appliance manufacturer wants to know if there is a relationship between percentage change in deposable personal income which is reported quarterly by the government, and the percentage change in appliances sold by the manufacturer following same years of quarterly data. Brenda Chee and Clarence Paulus lead an analyst team has obtained data for the past 10 quarters. (Hint: Provides your answers in two decimal points) (a) What forecasting model should be used for this data. Why? (b) Develop the forecasting model that you have proposed in (a). (c) Compute the relationship for these data. In your opinion, is the relationship between independent variable strong enough to base a predictive the dependent variable? Explain your answer.
1. Forecasting stationary series The company Handy Inc. produces a solar-powered electronic calculator that has experienced the following monthly sales history for the first four months of the year (in thousands of units): January February. March. April sales 23.3. 72.3. 30,3. 15.5 a) Find the forecast for May using the Moving Averages method with N = 3. b) Assume that the forecast for January was 25. Determine the one-step ahead forecast for February through May, using exponential smoothing with a smoothing constant of α = 0.15. c) Repeatthecalculationinproblembforavalueofα=0.40. d) Compare the MAD (Mean Absolute Deviation) and MSE (Mean Squared Error) for the forecast for February to April in problems b and c. Comment the accuracy of the forecasts. e) The demand in May turned out to be 30. Which of the three forecasting method were most accurate for this prediction? 2. Regression analysis 3 000 000 2 500 000 2 000…