STP452topic3

# STP452topic3 - STAT 512 Applied Regression Analysis Topic 3...

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STAT 512: Applied Regression Analysis Topic 3 Spring 2008 Chapter 5: Linear Regression in Matrix Form The SLR Model in Scalar Form Y i = β 0 + β 1 X i + ² i , ² i iid N (0 2 ) . Consider now writing an equation for each observation: Y 1 = β 0 + β 1 X 1 + ² 1 Y 2 = β 0 + β 1 X 2 + ² 2 . . . . . . . . . Y n = β 0 + β 1 X n + ² n The SLR Model in Matrix Form Y 1 Y 2 . . . Y n = β 0 + β 1 X 1 β 0 + β 1 X 2 . . . β 0 + β 1 X n + ² 1 ² 2 . . . ² n = 1 X 1 1 X 2 . . . . . . 1 X n β 0 β 1 + ² 1 ² 2 . . . ² n where (we use bold symbols for matrices and vectors) X is called the design matrix β is the vector of parameters ² is the error vector Y is the response vector 1

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The design matrix X = 1 X 1 1 X 2 . . . . . . 1 X n R n × 2 Vector of parameters β = β 0 β 1 R 2 × 1 Vector of error terms ² = ² 1 ² 2 . . . ² n R n × 1 Vector of responses Y = Y 1 Y 2 . . . Y n R n × 1 And we have Y = X β + ² . Variance-Covariance Matrix In general, for any set of variables U 1 ,U 2 ,...,U n , their variance-covariance matrix is de ned to be σ 2 { U } = σ 2 { U 1 } σ { U 1 ,U 2 } ... σ { U 1 ,U n } σ { U 2 ,U 1 } . . . . . . . . . σ { U n - 1 ,U n } σ { U n ,U 1 } ... σ { U n ,U n - 1 } σ 2 { U n } where σ 2 { U i } is the variance of U i , and σ { U i ,U j } is the covariance of U i and U j . When variables are uncorrelated, that means their covariance is 0. The variance-covariance matrix of uncorrelated variables will be a diagonal matrix, since all the covariances are 0. 2
Note: Variables that are independent will also be uncorrelated. So when variables are correlated they are automatically dependent. However, it is possible to have variables that are dependent but uncorrelated, because correlation only measures linear dependence. A nice thing about normally distributed random variables is that they are a convenient special case: if they are uncorrelated they are also independent. Covariance matrix of ² σ 2 { ² } = Cov ² 1 ² 2 . . . ² n = σ 2 I n × n = σ 2 0 ... 0 0 σ 2 0 . . . . . . . . . 0 0 ... σ 2 Covariance matrix of Y σ 2 { Y } = Cov Y 1 Y 2 . . . Y n = σ 2 I n × n . Distributional Assumptions in Matrix Form ² N ( 0 2 I ) where I is the n × n identity matrix. Ones in the diagonal elements specify that the variance of each

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STP452topic3 - STAT 512 Applied Regression Analysis Topic 3...

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