Consider the following time series data.Month 1 2 3 45 6 725 14 21 13 20 24 16Value(a) Compute MSE using the most recent value as the forecast for the next period.MSE = 8.97xWhat is the forecast for month 8?13X(b) Compute MSE using the average of all the data available as the forecast for the next period. (Round your answer to two decimal places.)MSE 8XWhat is the forecast for month 8?65X(c) Which method appears to provide the better forecast?O The average of all the previous values provides a better forecast, because its forecast for month 8 is larger than the month 8 forecast using the most recent value.O The most recent value provides a better forecast, because its forecast for month 8 is smaller than the month 8 forecast using the average of all the previousvalues.O The most recent value provides a better forecast, because its MSE is smaller than the MSE using the average of all the previous values.The average of all the previous values provides a better forecast, because its MSE is smaller than the MSE using the most recent value.

Given the time series data:- Month 1 2 3 4 5 6 7 Value 25 14 21 13 20 24 16

Answered: Consider the following time series…

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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section 5.4 Step 1 of 2 : Calculate the population mean for the shelf-life. Round your answer to two decimal places, if necessary. Step 2 of 2 : Calculate the population standard deviation for the shelf-life. Round your answer to two decimal places, if necessary.
Use the gas prices data set below to calculate a 3-month Weighted Moving Average (WMA) forecast for the average gas prices and predict the average retail gasoline price for February 2015. Use the weights of 0.6, 0.3, and 0.1 for the 3-month WMA, where the first weight is used for the most recent month and the last weight is used for the least recent month. Round your answer to two decimal places, if necessary. Month-Year Jun-14 Jul-14 Aug-14 Sep-14 Oct-14 Nov-14 Dec-14 Jan-15 Average Gas Price $3.766 $3.688 $3.565 $3.484 $3.255 $2.997 $2.632 $2.208 Copy Data
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Price of eggs and milk: The following table presents the average price in dollars for a dozen eggs and a gallon of milk for each month from January through October 2007. Use a TI-84 calculator to answer the following. Dozen Eggs Gallon of Milk 1.30 3.16 1.28 3.12 1.21 3.07 1.24 3.00 1.21 3.08 1.24 3.02 1.25 3.05 1.26 3.06 1.35 2.99 1.54 3.00 Compute the least-squares regression line for predicting the price of milk from the price of eggs. Round the slope and y-intercept to at least four decimal places.
The numbers of polio cases in the world are shown in the table for various years. Year Number of Polio Cases (thousands) 1988 1992 1996 2000 2005 2007 350 138 33 3 3.2 1.3 Let f(t) be the number of polio cases (in thousands) t years after 1980. Copy the data to Desmos to draw a scatterplot of the data. Make sure to adjust the years so they represent years after 1980. a) Is it better to model the data with a linear or with an exponential model? [Select an answer b) Use regression in Desmos to find an appropriate model for f. Make sure to check Log mode in the parameter window in Desmos if you are fitting an exponential model. f(t) Round the coefficients to 4 decimal places. Hint: Use the variablet from the drop down menu when entering the equation. Do not just type in t. c) Predict the number of polio cases in 2013. d) Find the approximate half-life of the number of polio cases. Round the answer to one decimal place. years
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Consider the following table that gives the population of City A from Year 1 to Year 6. Complete all parts (a-k) to this question. Part 1: Fill-in the last two columns in the table. List your answers as a - j for the chart and answer the question k below. Round growth rate to the nearest ten-thousandth (4 decimal places). Population of City A, Year 1 to year 6 Year Population (P) Change ΔP{"version":"1.1","math":"<math xmlns="http://www.w3.org/1998/Math/MathML"><mo>∆</mo><mi>P</mi></math>"} Growth Rate ΔPP{"version":"1.1","math":"<math xmlns="http://www.w3.org/1998/Math/MathML"><mfrac><mrow><mo>∆</mo><mi>P</mi></mrow><mi>P</mi></mfrac></math>"} 1 315,422 N/A N/A 2 320,421 a. b. 3 321,765 c. d. 4 322,162 e. f. 5 331,165 g. h. 6 331,200 i. j. Part 2: k. Does it make sense to fit this data with an exponential model? Why or why…

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