Consider the following regression models:A. Y₁ = ao + a₁ X₁i + uiB. Y = Bo + B1X1i + B2X2i + B3 X3i + eiLet di be the OLS estimator of a computed from Model A, andlet 1 be the OLS estimator of 3₁ computed from Model B.Which of the following statements is/are CORRECT?If Model B is the 'true' model, then E (a1) # a1 unless X₁; isuncorrelated with Xx2; and x31.□ If Model A is the 'true' model, then Var (₁) ≤ Var (₁).If Model B is the 'true' model, and the explanatory variables aredeterministic, then Var (₁) ≤ Var (B₁).B₁ unless x₁ is(6₁) + B₁ unless x1, is□ If Model A is the 'true' model, then E (B₁)uncorrelated with X2; and X3;.

Given the regression models:A. B.

Answered: Consider the following regression…

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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The Simple Linear Regression model is Y = b0 + b1*X1 + u and the Multiple Linear Regression model with k variables is: Y = b0 + b1*X1 + b2*X2 + ... + bk*Xk + u Y is the dependent variable, the X1, X2, ..., Xk are the explanatory variables, b0 is the intercept, b1, b2, ..., bk are the slope coefficients, and u is the error term, Yhat represents the OLS fitted values, uhat represent the OLS residuals, b0_hat represents the OLS estimated intercept, and b1_hat, b2_hat,..., bk_hat, represent the OLS estimated slope coefficients. QUESTION 7 In the MLR model, the assumption of ‘linearity in parameters’ is violated if: one of the slope coefficients appears as a power (e.g. Y = b0 + b1*(X1^b2) + b3*X2 + u) the model includes the reciprocal of a variable (e.g. 1/X1) the model includes a variable squared (e.g. X1^2) the model includes a variable in its logarithmic form (i.e. log(X1) ) QUESTION 8 In the MLR model, the assumption of 'no perfect collinearity'…

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