The covariance matrix of an image with three spectral components is shown below. Let x₁, x₂, and x3 denote the spectral components of each pixel in the image. Finda new variable of the form y₁ = C₁X₁ + C₂X₂ + C3x3 that has maximum possible variance, subject to the constraint that c3 + c3 + c3 = 1. What percentage of the totalvariance in the data is explained by y₁?27.22 19.55 6.00S= 19.55 21.88 13.366.00 13.36 25.32(...)Which of the following could be the new variable y₁?O A. Y₁ = -0.56x₁ +0.06x2 -0.83x3O C.y₁ = 0.62x₁ +0.62x2 +0.62x3O B. y₁ = -0.54x₁ -0.78x2 +0.32x3ⒸD. y₁=0.54x₁1 -0.78x2 + 0.32x3O F. y₁ = 0.62x₁ +0.63x2 +0.47x3y₁ = 0.62x₁ +0.63x2 -0.47x3O E. y₁=0.54x₁1 -0.78x2 -0.32x3O G. y₁ = 0.56x₁ +0.06x2 -0.83x3O H.

The new variable of Y1 will be y1=0.54x1-0.78x2+0.32x3.

Answered: The covariance matrix of an image with…

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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Which of the following is true about the correlation coefficient? A) It ranges from 0 to 1. B) It indicates the strength and direction of a linear relationship between two variables. C) It can be greater than 1. D) It is used to measure the central tendency of data.
Suppose a local university researcher wants to build a linear model that predicts the freshman year GPA of incoming students based on high school SAT scores. The researcher randomly selects a sample of 40 sophomore students at the university and gathers their freshman year GPA data and the high school SAT score reported on each of their college applications. He produces a scatterplot with SAT scores on the horizontal axis and GPA on the vertical axis. The data has a linear correlation coefficient of 0.481202. Additional sample statistics are summarized in the table below. Variable Sample Sample standard Variable description mean deviation high school SAT score x = 1495.716802 Sx = 109.915203 y freshman year GPA y = 3.260911 Sy 0.492802 r = 0.481202 slope = 0.002157 Determine the y-intercept, a, of the least-squares regression line for this data. Give your answer precise to at least four decimal places. a =
Psi is a measure of compressive strength, or the ability of the material to carry loads and handle compression. The desired concrete psi rating used for sidewalks and residential driveways ranges from 2500psi to 3000psi obtained from mixing cement, stone, and sand in different ratios but with the same amount of water. The summary of the psi's of three (3) such concrete mixes made by three (3) different civil engineering students is given as follows: Mean vector: The variance-covariance matrix: Concrete mix Mean Concrete mix 1 Concrete mix 2 Concrete mix 3 Concrete mix 1 | Concrete mix 2 2700 18 12 2 3000 12 16 3 2400 Concrete mix 3 16 25 Let the random variable Y be the vector of the psi's of concrete mixes obtained by the students, i.e. Y1, denotes the psi of concrete mix made by student 1, Y2 is the psi of concrete mix made by student 2 and Y3 is the psi of concrete mix psi made by student 3. (a) Find (i) the multivariate probability distribution function (pdf) of Y. (ii) the…
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Consider an AR(2) model: STEPS; -Write the equation of the AR(2) model- Determine the model based on the delay operator.- Find the expected value of the model- Find the variance of the model.- Find the covariances associated with 1,2, and s steps.- Find the associated correlation indices of 1,2, and s steps.- Assume that information is available up to time $h$ and the function that contains the accumulated information f(h, h-1,……), determine the forecasts and the error associated with 1,2, and s steps.- Assume that there is an initial value of the returns, denoted by $r_0$, and we have the AR(2) model at time rt, convert the AR(2) model into its equivalent MA(t)
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Consider an MA(2) model: STEPS -Write the equation of the M(2) model-Determine the model based on the delay operator.- Find the expected value of the model- Find the variance of the model.- Find the covariances associated with 1,2, and s steps.- Find the associated correlation indices of 1,2, and s steps.- Assume that information is available up to time $h$ and the function that contains the accumulated information f(h, h-1,……), determine the forecasts and the error associated with 1,2, and s steps.
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