A store has the following demand figures for the last four years:Year1234Demand100150112200Given the demand forecast for year 2 of 100, the trend forecast for year 2 of 10, an alpha of 0.3, and a betaof 0.2, what is the demand forecast for year 5 using the double exponential smoothing method?0125O 134O 166O Cannot be determined

The store's demand figures for the last years:The demand forecast for year The trend forecast for…

Answered: A store has the following demand…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Example: Emergency call to the fire department over the past 15 weeks are the following: 20, 27, 25, 22, 18, 21, 26, 19, 16, 28, 25, 26, 17, 23, 27 Use exponential smoothing with a smoothing constant of 0.5 to forecast the number of the calls for the next week. Solution: No, of Calls (At) Forecast (Actual time] Week (Ft) 20 2 27 3. 25 22 18 21 7 26 8. 19 6. 16 10 28 11 25 12 26 13 17 14 23 15 27 Find F16. Ft-1 + a(At-1- Ft-1) %3D | Forecast = previous forecast + a(Actual previous period - previous forecast) Put F1 = A1 = 20 %3D F2 = F, + a(A, - F,) = 20 +0.5(20-20) = 20 F3 = 23.5 %3D
Below is data collected over 6 specific years. The data collected is the Consumer Price Index (CPI) and the cost of a slice of tuna. We would like to build a model using the CPI to predict the cost of a slice of pizza in a given year. Year 1960 1973 1986 1995 2002 2003 CPI (x) 30.2 48.3 112.3 162.2 191.9 197.8 Cost of a slice of tuna(y) 0.15 0.35 1.00 1.25 1.75 2.00 Plot the data. Are we justified in creating a Linear Regression model to predict the price of a pizza slice using the CPI? Why or why not? Calculate the descriptive statistics. Calculate the sums of squares Calculate the slope, intercept, and correlation. What is this correlation value telling us? Assemble the prediction equation, and interpret the slope within the context of the problem. State the hypotheses for the hypothesis test for regression.
Let's say you are playing the stock market and below period 2020 data was provided. For "stock A" you use a 2 month moving average. For "stock B" you use exponential smoothing with (a= 0.3). Stock A Stock B ... Jan 0.11 Jan 14 Feb 0.20 Feb 11 Mar 0.03 Mar 6 Apr May Apr 1.20 May 0.50 Jun 0.03 Jun 12 Jul 0.10 Jul 16 Aug 011 Aug 14 Sep 0.56 Sep Oct 0.78 Oct Nov 0 44 Nov Dec 0.10 Dec What is the MAPE for Stock B? 276 A B. 62% 3.979 999
An arborist creates a table of different tree height patterns over a 4 year period. Time Fig Palm Maple 0 8 7 5 1 14 9.8 6 2 20 13.7 10.2 3 26 19.2 17.3 Which of the trees has an exponential growth pattern? Which of the following statements are true? Select all that apply.A. For exponential models, the difference between consecutive entries in the table is a constant.B. For exponential models, the relative change from one time to the next is always a constant.C. For exponential models, the absolute change from one time to the next is always a constant.D. For exponential models, the ratio of consecutive entries in the table is a constant.E. None of the above statements is true.
The following table contains the demand from the last 10 months: MONTH ACTUAL DEMAND 1 31 2 34 3 33 4 35 5 37 6 36 7 38 8 40 9 40 10 41 a. Calculate the single exponential smoothing forecast for these data using an α of 0.30 and an initial forecast ( F1�1 ) of 31. Note: Round your intermediate calculations and answers to 2 decimal places. b. Calculate the exponential smoothing with trend forecast for these data using an α of 0.30, a δ of 0.30, an initial trend forecast ( T1�1 ) of 1, and an initial exponentially smoothed forecast ( F1�1 ) of 30. Note: Round your intermediate calculations and answers to 2 decimal places. c-1. Calculate the mean absolute deviation (MAD) for the last nine months of forecasts. Note: Round your intermediate calculations and answers to 2 decimal places. c-2. Which is best? multiple choice Exponential smoothing with trend forecast Single exponential smoothing forecast
With the gasoline time series data from table 17.1, show the exponential smoothing forecasts using a = .1.a. applying the MSe measure of forecast accuracy, would you prefer a smoothing constant of a = .1 or a = .2 for the gasoline sales time series?b. are the results the same if you apply Mae as the measure of accuracy?c. What are the results if Mape is used?
A pharmacist has been monitoring sales of a certain over-the-counter pain reliever. Daily sales during the last 15 days were Day: Number sold: 2 8. 37 40 45 44 48 49 47 46 50 10 11 12 13 14 15 Day: Number sold: 54 52 53 57 49 53 a. Assume the data refer to demand rather than sales. Using trend-adjusted exponential smoothing with an initial forecast of 42 for Day 8, an initial trend estimate of 2, and a = ß = .3, develop demand forecasts for Days 9 through 16. Then compute the resultant MSE using the error values from Days 8 through 15. (Round your intermediate period-by-period forecast and error values to 3 decimal places. Round your final MSE answer to 3 decimal places.)
Below is a table containing data on product demand for the most recent five months along with the forecasts that had been made for those 5 previous months. Use the exponential smoothing method to forecast the number of sales to expect next month. Use the following value of alpha: 0.2. Month Demand Forecast 1 308 349.2 2 388 340.9 3 344 350.3 4 400 349.1 5 341 359.3
Nitrogen dioxide, NO2, is an air pollutant resulting from burning fossil fuels in cars and power plants. The following table shows the decrease in the national average of the concentration in parts per billion (ppb) of NO2 for selected years. Year 2014 2015 2016 2017 2018 NO2 (ppb) 40.8107 39.7886 38.7972 37.8209 36.8752 Use exponential regression to find an exponential decay function for the concentration, C, of NO2 t years after 2014. Round constants to the nearest ten-thousandth. C = Use the model to predict the concentration of NO2 (in ppb) in 2050. Round to the nearest whole number. ? ppb

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