y = X,B1 + X,B2 + ɛ where y = (y1, .., Yn)', X1 and X2 are random except the intercept term (i.e., th vector of 1) included in X1. Conditional on Xı and X2, the random error vecte ɛ is jointly normal with zero expectation and variance-covariance matrix V, which does not depend on X1 and X2. V is not a diagonal matrix (i.e., some off-diagonal elements are nonzero). B1 and B2 are vectors of two different sets of regression coefficients; B1 has two regression coefficients and B2 has four regression coefficients. B = (B1 , B½)'; that is, B is a column vector of %3D six regression coefficients. a) V is completely known (i.e., the values of all elements of V are given).

y = X,B1 + X,B2 + ɛ where y = (y1, .., Yn)', X1 and X2 are random except the intercept term (i.e., th vector of 1) included in X1. Conditional on Xı and X2, the random error vecte ɛ is jointly normal with zero expectation and variance-covariance matrix V, which does not depend on X1 and X2. V is not a diagonal matrix (i.e., some off-diagonal elements are nonzero). B1 and B2 are vectors of two different sets of regression coefficients; B1 has two regression coefficients and B2 has four regression coefficients. B = (B1 , B½)'; that is, B is a column vector of %3D six regression coefficients. a) V is completely known (i.e., the values of all elements of V are given).

Linear Algebra: A Modern Introduction

4th Edition

ISBN:9781285463247

Author:David Poole

Publisher:David Poole

Chapter7: Distance And Approximation

Section7.3: Least Squares Approximation

Problem 31EQ

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