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. Find a new variable of the form y₁ = C₁x₁ + C₂x₂ + C3x3 that has maximum possible variance, subject to the constraint that c² + c3 + c3 =1. What percentage of the total variance in the data is explained by y₁? 27.22 19.55 6.00 S 19.55 21.88 13.36 6.00 13.36 25.32 (...)

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…

Which scatterplot shows a nonlinear association?

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)

If a predictor variable (x) is highly correlated with another predictor variable (x), the variables are providing essentially the same information. This is best referred to as multi-collinearity. Group of answer choices True False

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.

Given data points (1, 3), (0, -1). (2. 1). (4. 1). (-2. 1), center the data and compute the sample covariance matrix. Record the entries of the covariance matrix row by row, separated by commas.