For the data below, answer the following four questions:Period Demand158259360461What would be the simple moving average forecast for the 5th period?68.25860.759.561

Forecasting is done to predict the actuals in the upcoming periods .There are several quantitative…

Answered: For the data below, answer the…

Contemporary Marketing

18th Edition

ISBN:9780357033777

Author:Louis E. Boone, David L. Kurtz

Publisher:Louis E. Boone, David L. Kurtz

Chapter14: Pricing Strategies

Section14.2: Forecasting Demand

Problem 1LO

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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 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 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_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?
Given the actual demand for years 2013/14, calculate the total forecast for year 2015, as well as the forecast units for each quarter of 2015. For your calculations use the principles of trend and seasonality. List five figures, as follows: Total, Q1, Q2, Q3, Q4 Quart. Year 2013 % Year 2014 % Year 2015 % 1 16 20 2 33 34 3 58 70 4 33 32
The following equation summarizes the trend portion of quarterly sales of condominiums over a long cycle. Sales also exhibit seasonal variations. Ft = 51 − 4.1t + 3.1t 2 whereFt = Unit sales t = 0 at the first quarter of last year Quarter Relative 1 0.95 2 0.95 3 0.45 4 1.65 Click here for the Excel Data File Using the information given, prepare a forecast of sales for each quarter of next year (not this year), and the first quarter of the year following that. (Round intermediate calculations and final answers to 2 decimal places.)
After using your forecasting model for six months, you decide to test it using MAD and a tracking signal. Here are the forecast and actual demands for the six-month period: PERIOD FORECAST ACTUAL May 450 480 June 500 560 July 550 380 August 590 465 September 635 650 October 715 610 a. Find the tracking signal for each month.
Given the following historical data, what is the simple three-period moving average forecast for period 6? Period Demand 1 73 2 71 3 72 4 73 66 5 (Keep two decimal places in your answer)
Given the following historical data, what is the moving-average forecast for period 6 based on the last three periods?Period Value1 332 283 254 325 27Did is solve correctly I tried it in excel is 29.5 corrector do avg(25+32+27)/3 =29Please advise?
Apply the 3-years moving average method to forecast the required demand for the year 2021 from the following historical data. Years 2012 2013 2014 2015 2016 2017 2018 2019 2020Demand 245 325 300 350 295 400 450 380 500

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For the data below, answer the following four questions: Period Demand 1 58 59 60 61 What would be the simple moving average forecast for the 5th period? O 68.2 O 234 58 60.7 59.5 61