Can you assit me with this problem 3 by showing me the process step by step. I prefer it not be in the form of an excel sheet because it is hard to follow along. Thank you kindly The following table shows the actual demand observed over the last 11 years:Year234781011Demand7104713711148137This exercise contains only parts b, c, and d.b) Using the 3-year moving average, provide the forecast from periods 4 through 12 (round your responses to one decimal place).Year46.78101112Forecastc) Using the 3-year weighted moving average with weights 0.15, 0.25, and 0.60, using 0.60 for the most recent period, provide the forecast from periods 4 through12 (round your responses to two decimal places).Year4789101112Forecastd) Mean absolute deviation for the forecast developed using the 3-year moving average is 2.76 and for the 3-year weighted moving average is 3.61. Based on thisinformation, the better forecast is achieved using the weighted moving average approach.

Forecast for Year 4 = (Sum of Demand of Year 3,Year 2, Year 1) / 3 Year 4 5 6 7 8…

The following table shows the actual demand observed over the last 11 years: Year Demand 1 2 7 10 3 4 4 7 11 10 13 7 5 6 7 9 13 7 11 14 8 This exercise contains only parts b, c, and d. ) Using the 3-year moving average, provide the forecast from periods 4 through 12 (round your responses to one decimal place). 4 5 6 7 8 9 Year 10 11 12 Forecast :) Using the 3-year weighted moving average with weights 0.15, 0.25, and 0.60, using 0.60 for the most recent period, provide the forecast from periods 4 through 12 (round your responses to two decimal places). Year 10 12 co

The following table shows the actual demand observed over the last 11 years: Year Demand 1 2 7 10 3 4 4 7 11 10 13 7 5 6 7 9 13 7 11 14 8 This exercise contains only parts b, c, and d. ) Using the 3-year moving average, provide the forecast from periods 4 through 12 (round your responses to one decimal place). 4 5 6 7 8 9 Year 10 11 12 Forecast :) Using the 3-year weighted moving average with weights 0.15, 0.25, and 0.60, using 0.60 for the most recent period, provide the forecast from periods 4 through 12 (round your responses to two decimal places). Year 10 12 co

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 22P: The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six...

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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 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_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.
Under what conditions might a firm use multiple forecasting methods?
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_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_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?
Do the sales prices of houses in a given community vary systematically with their sizes (as measured in square feet)? Answer this question by estimating a simple regression equation where the sales price of the house is the dependent variable, and the size of the house is the explanatory variable. Use the sample data given in P13_06.xlsx. Interpret your estimated equation, the associated R-square value, and the associated standard error of estimate.
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_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?
Management of a home appliance store wants to understand the growth pattern of the monthly sales of a new technology device over the past two years. The managers have recorded the relevant data in the file P13_05.xlsx. Have the sales of this device been growing linearly over the past 24 months? By examining the results of a linear trend line, explain why or why not.
The manager of Carpet City outlet needs to make an accurate forecast of demand for Soft Shag Carpet (its biggest seller). If the manager does not order enough carpet from the carpet mill, customers will buy their carpet from one of Carpet City’s many competitors. The manager has collected the following demand data for the past 8 months: Month Demand for Soft Shag Carpet (1000 yd) 1 8 2 12 3 7 4 9 5 15 6 11 7 10 8 12 Required showing all workings: Compute a 3-month moving average forecast for months 4 through 9. Compute a weighted 3-month moving average forecast for months 4 through 9. Assign weights of 0.55, 0.33 and 0.12 to the months in sequence, starting with the most recent month.