8. (a) Assume process X, satisfies X,-aX-1+ Z where Z, is WN(0, a?) and Ja< 1. Usethe Yule-Walker equation to find the best linear predictor P(X+iXn Xn-1). Find themean-square error of the predictor.= Z, + aZ,-1 + bZ,-2, where Z, is WN(0, a). Use the(b) Assume process X, satisfies X,Durbin-Levinson algorithm to find the linear predictor P(X+i|X Xn-1)-

This question is about finding the best linear predictors for a given autoregressive process. The…

Answered: 8. (a) Assume process X, satisfies X,…

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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Exercise 4. (a) x²(5.7) ~ Gamma(?,?), (b) x²(8) ~ Gamma(?,?), (c) - Gamma(4, 3) ~ x² (?)
The line of best fit through a set of data isy=46.539+4.023xy=46.539+4.023xAccording to this equation, what is the predicted value of the dependent variable when the independent variable has value 40?y = Round to 1 decimal place.
Suppose the PRF of two-variable regression is represented in the equation (1) Y₁ =b₁ + b₁X₁ + u₁, u~i.i.d N(0,0²) (1) We use Ordinary Least Square (OLS) method to estimate the coefficients in (1), The OLS estimated SRF is represented in the equation (2) Y₁ =B₁ + B₁X₁ + e, (2) In which , and B, are OLS estimates of b, and b, respectively. (2) Prove Σe, = 0 (3) Prove Sex = 0
7
38. Given the power regression model y = 40x^1.3, which is the linear regression model after transforming to a log-log graph? Y = 1.3X + 1.60 Y = 0.20X + 0.11 Y = 0.20X + 0.36 Y = 1.60X + 0.11
Q12. Suppose we are running gradient descent to fit a logistic regression model with 0 E R*+1. Suggest one way to check whether the learning rate « is set properly and that gradient descent is running correctly.
In a study, the simple linear regression equation was found as y = - 2.65 + 3.23 * x. Accordingly, if the value of x is 1.55, what will be the value of "y"? Biraraştımada basit doğrusal regresyon denklemi y-265+3,23xolarak bulunmuştur. Buna yöre xin değeri 1,55 olursa y'nin değeri ne olur?- 25 - O A) -2,36 O B) 2,36 O C) 6,32 O D) -7,66 O E) 7,66
Suppose we have a multiple regression model with 2 predictors and an intercept. (Without any interaction or higher order terms, we have only the 2 predictors in the model and the intercept.) We have only n= 6 observations (so it would be rather silly to fit this model to this data, but let's pretend it is reasonable). We find the values of the first 5 residuals are: 2.6, 2.3, 2.5, -1.5, -1.4 What is the value of MSRes for this multiple regression model?
General Directi0n: F0r activities that inv0lve s0luti0n to problems, perform the steps in conducting hypothesis testing such as stating of problems and hypothesis, and so on.
Consider the following population model for household consumption: cons = a + b1 * inc+ b2 * educ+ b3 * hhsize + u where cons is consumption, inc is income, educ is the education level of household head, hhsize is the size of a household. Suppose a researcher estimates the model and gets the predicted value, cons_hat, and then runs a regression of cons_hat on educ, inc, and hhsize. Which of the following choice is correct and please explain why. A) be certain that R^2 = 1 B) be certain that R^2 = 0 C) be certain that R^2 is less than 1 but greater than 0. D) not be certain
The subsets of {1,2} are Φ, {1}, {2} and {1,2}. So there are four possible potential models when deciding on a regression with a choice of inputs from a dataset containing two potential input variables. Your friend tells you this is nonsense and there are three possible models because the empty set Φ is just an imaginary concept concocted by some mathematician. What should you say to your friend? You should explain that the empty set corresponds to the model y = β + ε, where β is some constant and ε is a residual term. In this model, the output predictions are always equal to the output's sample average. You should explain that the empty set corresponds to the model y = ε, where ε is a residual term. In this model, the output predictions are totally random. You should indeed agree. Your friend is spot on and one cannot have a regression with no input variables. You should partially agree. Your friend is spot on that there are three models and not…
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