The following table shows the actual demand observed over the last 11 years:Year1234567891011Demand7951013711 138138Using exponential smoothing with α = 0.40 and a forecast for year 1 of 6.0, provide the forecast fromperiods 2 through 12 (round your responses to one decimal place).Year123456789101112Forecast 6.0Provide the forecast from periods 2 through 12 using the naive approach (enter your responses as wholenumbers).Year23456789101112Forecast

Given that: α=0.40 Forecast for year 1 = 6 Year 1 2 3 4 5 6 7 8 9 10 11 Demand 7 9 5 10 13 7 11…

Answered: The following table shows the actual…

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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Based on the following demand data, Glass Inc Company performs double exponential smoothing. Period 1 180 Demand 2 3 4 5 6 7 125 150 146 155 149 154 150 Assume the value a = 0.1, B = 0.15 Also assume the initial forecast value Fo = 135, Go = 20. Use double exponential smoothing. a. Find the following forecast values. F3 = F4 = Fs = F6 = b. Find the following if F1 = 145 and a = 0.2. F2,5 = F7,10 = c. Find the forecast error of first 6 values, then find MAD using these values. ej = e2 = ez = e4 = es = e6 = MAD =
The following table shows the actual demand observed over the last 11 years: 1 2 3 4 5 6 7 8 9 10 11 Year Demand 6 8 6 10 12 7 12 13 8 11 8 Using exponential smoothing with α = 0.40 and a forecast for year 1 of 5.0, provide the forecast from periods 2 through 12 (round your responses to one decimal place). 3 4 5 6 7 8 9 10 11 12 Year 1 2 Forecast 5.0 Provide the forecast from periods 2 through 12 using the naive approach (enter your responses as whole numbers). Year 2 3 4 5 6 Forecast 7 8 9 10 11 12
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.
the table shows the percent of households with internet access for selected years from 2009 and projected through 2015. Year Percent of households 2009 67 2010 70 2011 72.5 2012 75 2013 76.5 2014 77.2 2015 78 Use the model to predict the percent of households with internet access in 2022.
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
The following table shows the actual demand observed over the last 11 years 1 Year Demand 7 2 8 2 Year Forecast 3 3 4 4 5 10 12 4 4 6 8 5 6 7 11 Using exponential smoothing with a = 0.30 and a forecast for year 1 of 6.0, provide the forecast from periods 2 through 12 (round your responses to one decimal place). Year 1 Forecast 6.0 8 12 7 8 ☐ ☐ 9 9 8 10 11 10 11 6 Provide the forecast from periods 2 through 12 using the naive approach (enter your responses as whole numbers). 2 3 5 6 7 8 9 10 11 12 0 11 12 H
The Jersey Dairy Products Company produces cheese, which it sells to supermarkets and foodprocessing companies. Because of concerns about cholesterol and fat in cheese, the company has seen demand for its products decline during the past decade. It is now considering introducing some alternative low-fat dairy products and wants to determine how much available plant capacity it will have next year. The company has developed an exponential smoothing forecast with a = .40 to forecast cheese demand. The actual demand and the forecasts from the model are as follows:Assess the accuracy of the forecast model by using MAD and cumulative error. If the exponential smoothing forecast model does not appear to be accurate, determine whether a linear trend model would provide a more accurate forecast.
The following are historical demand data: ACTUAL YEAR 2011 SEASON DEMAND Spring 201 Summer 142 Fall 380 Winter 578 2012 Spring 469 Summer 273 Fall Winter 683 963 Use regression analysis on deseasonalized demand to forecast demand in summer 2013. (Do not round intermediate calculations. Round your answer to the nearest whole number.) Forecast for summer 2013
Develop a three-period moving average forecast for periods 13-15. Period 10 11 12 13 14 Demand 265 359 436 261 462 Calculate the forecasts for periods 13-15 by using a three-period moving average model (enter your responses rounded to the nearest whole number). Period Demand Forecast 10 265 11 359 12 436 13 261

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