=5. An AR(3) model has been fit to a time series. The estimates are ûand $3 = 0.1. The last four observations were Yn-3 104, Yn-2 ===Yn 99. Forecast Yn+1 and Yn+2 using these data and estimates.102, 1 = 0.5, 2 = 0.2,101, Yn-1==102, and

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Answered: 5. An AR(3) model has been fit to a…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

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The following table gives the millions of metric tons of carbon dioxide emissions in a certain country for selected years from 2010 and projected to 2032. 2012 2014 2016 2018 2020 CO2 Emissions 338.5 364.5 397.1 423.8 452.1 498.4 Year 2010 Year 2022 2024 2026 2028 2030 CO2 Emissions 557.2 592.9 628.7 662.1 707.1 (a) Create a linear function that models these data, with x as the number of years past 2010 and y as the millions of metric tons of carbon dioxide emissions. (Round all numerical values to two decimal places.) y(x) = 2032 741.7 (b) Find the model's estimate for the 2024 data point. (Round your answer to two decimal places.) million metric tons Interpret the slope of the linear model. For each year since ---Select--- (c) Find the slope of the linear model. (Round your answer to two decimal places.) , carbon dioxide emissions in the U.S. are expected to change by million metric tons.
b. Use a = 0.2 to compute the exponential smoothing forecasts for the time series (to 2 decimals). Time-Series Value Month Forecast 1 110 2 130 120 4 110 5 95 6. 125 7 140 8 140 9 105 10 80 11 100 12 110 13
The temperature of a certain chemical process is thought to be related to the time it takes for the process to reach completion. Out goal is to that relationship so that we can use it to predict TIME TO REACH COMPLETION at various TEMPERATURES. 14 trials are set up and the time for the process to complete is measured for each of the different temperatures. The data is presented here:(data is in the image added to the question as well as more questions to answer)
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?
Listed below are the numbers of deaths resulting from motor vehicle crashes. Let x represent the year, with 1975 coded as x = 1, 1980 coded as x = 6, 1985 coded as x = 11, and so on. Construct a scatterplot and identify the mathematical model that best fits the given data. Use the best model to find the projected number of deaths for the year 2028. x (year) 1975 1980 1985 1990 1995 2000 2005 2010 D y (deaths) 44,533 51,122 43,809 44,590 41,798 41,960 43,436 32,697 (...) Choose the correct graph below. O A. O B. O C. D. 52000- 52000- 52000- 52000- DDDD 00 X X X X 32000+ 32000 32000+ 32000+ 0 40 0 40 0 40 0 40 What is the equation of the best model? Select the correct choice below and fill in the answer boxes to complete your choice. Enter only nonzero values. (Type integers or decimals rounded to three decimal places as needed.) O A. y= X B. y = + In x O C. y = + O D. X y=² O E. y = x² + X + Use the best model to find the projected number of such deaths for the year 2028. The projected…
In an M/MA queueing system, the arrival rate is 5 customers per hour and the service rate is 9 customers per hour. If the service process is automated (resulting in no variation in service times but the same service rate), what will be the resulting performance measurements? (Round your answers to 3 decimal places.) 2.
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5. An AR(3) model has been fit to a time series. The estimates are û = 102, 1 = 0.5, 2 = 0.2, and 30.1. The last four observations were Yn-3 = 104, Yn-2 = 101, Y-1 = 102, and Yn99. Forecast Yn+1 and Yn+2 using these data and estimates.