. In the Bayside Fountain Hotel problem, compute an exponentially smoothed forecast with an α value of .20. According to the result from Excel and/or POM-QM, the forecast for the year 10 would be b. In the Bayside Fountain Hotel problem, for the exponentially
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a. In the Bayside Fountain Hotel problem, compute an exponentially smoothed
b. In the Bayside Fountain Hotel problem, for the exponentially smoothed forecast with an α value of .20, compute the mean absolute deviation (MAD) via Excel and/or POM-QM.
c. In the Bayside Fountain Hotel problem, compute an adjusted exponentially smoothed forecast with α = .20
and β = .20. According to the result from Excel and/or POM-QM, the forecast for the year 4 would be
d.
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- Plot the logarithm of arrivals for each transportation mode against time, all on the same graph. Which now appears to be growing the fastest?Q6 Suppose that at time t=0, half of a "logistic" population of 100000 persons have heard a certain rumor and that the number of those who have heard it is then increasing at the rate of 500 persons per day. How long will it take for this rumor to spread to 80% of the population? ( Suggestion: Find the value of k by substituting P(0) and P'(0) in the logistic equation) a. About 98 days b. About 34 days c. About 19 days d. About 69 days e. About 119 daysSuppose that you work for a U.S. senator who is contemplating writing a bill that would put a national sales tax in place. Because the tax would be levied on the sales revenue of retail stores, the senator has asked you to prepare a forecast of retail store sales for year 8, based on data from year 1 through year 7. The data are: (c1p2) Year Retail Store Sales 1 $1,225 2 1,285 3 1,359 4 1,392 5 1,443 6 1,474 7 1,467 54 Chapter One a. Use the first naive forecasting model presented in this chapter to prepare a forecast of retail store sales for each year from 2 through 8. b. Prepare a time-series graph of the actual and forecast values of retail store sales for the entire period. (You will not have a forecast for year 1 or an actual value for year 8.) c. Calculate the root-mean-squared error for your forecast series using the values for year 2 through year 7. 3. Use the second naive forecasting model presented in this chapter to answer parts (a) through (c) of Exercise 2. Use P 0.2 in…
- Consider the following time series data: Month 1 2 3 4 5 6 7 Value 24 13 20 12 19 23 15 Compute MSE using the most recent value as the forecast for the next period. What is the forecast for month 8? Compute MSE using the average of all the data available as the forecast for the next period. What is the forecast for month 8? Which method appears to provide the better forecast?When forecasting future sales based on the most recent years's total revenue growing at a revenue growth rate of of 21.85%, with the following years... Year 1 Sales = 209,098,000,000 Year 2 sales = 101,975,000,000 Year 3 sales = 187,879,000,000 Are you calculating it as Year 1 = 209,098 million x .2185 + 209,098,000,000 Year 2 = Year 1 sales answer x .2185 + 101,975,000,000 Year 3 = Year 2 sales answers x .2185 + 187,879,000,000Can you explain what these two belows mean in regard of GMM and Maximum likelihood. What are we calculating and what is it used to Unconstrained optimizationConstrained optimizatio
- In the past four years, the annual returns of one company’s stockare 12%, 18%, and –14%, and 7%.a) What is the geometric average return? b) What is the arithmetic average of the returns? c) According to an economist’ forecast on the Year 2020, the probabilities of repeatingthe performances of the former four years are 30%, 30%, 20%, and 20%, respectively.What is the expected return of the stock in the Year 2020Historical demand for Peeps is as displayed in the table. Month Demand January 11 February 18 March 31 April 39 May 44 June 53 July 67 August 82 September 96 Develop forecasts from June through October using these techniques: Holt's method with alpha=0.2 and beta=0.1. For Holt's model, the level and trend for May are assumed to be 44 and 12. Judge which forecast method is the best based on MAD.A researcher has a sample of 6 annual observations {94, 104, 102, 99, 111 and 107} for the CPI in country Z for the period 2015 to 2020, and wants to forecast CPI for the years 2021, 2022 and 2023. The researcher uses 3 different forecasting models: A, B and C. Model A is an AR(1) model with no drift and with an estimated autoregressive coefficient = 0.7. Model B is a MA(1) model with no constant and with an estimated MA coefficient = -0.4 (note the minus !). Model C is a random walk model with no drift. The error terms over the 2015-2020 period were estimated to have the values: {3, -1, 2, 4, -3, 1}. a. Compute the 2021, 2022 and 2023 forecasted values for the consumer price index based on the three models. Show the formulas and the details of your calculations, and explain all the related symbols. b. Suppose that the actual values of the CPI over the 2021, 2022 and 2023 were {108, 114, 105}. Calculate the Root mean square error of the three model forecasts over the 2021-2023…
- Bell Greenhouses has estimated its monthly demand for potting soil to be the following:N = 400 + 4Xwhere N = monthly demand for bags of potting soilX = time periods in months (March 2006 = 0)Assume this trend factor is expected to remain stable in the foreseeable future. The following table contains the monthly seasonal adjustment factors, which have been estimated using actual sales data from the past five years:MONTH ADJUSTMENT FACTOR (%)March +2June +15August +10December −12a. Forecast Bell Greenhouses’ demand for potting soil in March, June, August, and December 2007.b. If the following table shows the forecasted and actual potting soil sales by Bell Greenhouses for April in five different years, determine the seasonal adjustment factor to be used in making an April 2008 forecast.YEAR FORECAST ACTUAL 2007 500 515 2006 452…5 True/False/uncertain 7) With all observations in the same cluster, RSQ = 1. 8) With 500 multiple imputation samples generating 500 predictions of 0 or 1 for the binary dependent variable for each observation, to combine the 500 predictions into one for each observation, one has to take the mean of all 500 predictions and use a cutoff of 50%.Savings-Mart (a chain of discount department stores) sells patio and lawn furniture. Sales are seasonal, with higher sales during the spring and summer quarters and lower sales during the fall and winter quarters. The company developed the following quarterly sales forecasting model: Y t=8.25+0.125t2.75D1t+3.50D3t where Y t=predictedsales(million)inquartert 8.25=quarterlysales(million)whent=0 t=timeperiod(quarter)wherethefourthquarterof2002=0,firstquarterof2003=1,secondquarterof2003=2,... D1t={1forfirst-quarterobservations0otherwiseD2t={1forsecond-quarterobservations0otherwiseD3t={1forthird-quarterobservations0otherwise Forecast Savings-Marts sales of patio and lawn furniture for each quarter of 2010.