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Matrix Form Linear Regression Assumptions
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In this video I cover the assumptions of the Linear Regression Model as formulated by the Matrix Form.
The assumptions that I cover are the Expected Value of the Error Terms being 0, the Variance of the error terms being constant at sigma squared, the assumption that the error terms are uncorrelated and lastly that the error terms are Normally distributed.
By combining all these assumptions we can derive the distribution of the error terms in Matrix Form. This will allow us to prove the Unbiasedness of the OLS estimator as well as deriving the variance of the OLS estimators which will be used later on when dealing with inferential statistics of the OLS estimators.
#Econometrics
#MatrixForm
#Regression
The assumptions that I cover are the Expected Value of the Error Terms being 0, the Variance of the error terms being constant at sigma squared, the assumption that the error terms are uncorrelated and lastly that the error terms are Normally distributed.
By combining all these assumptions we can derive the distribution of the error terms in Matrix Form. This will allow us to prove the Unbiasedness of the OLS estimator as well as deriving the variance of the OLS estimators which will be used later on when dealing with inferential statistics of the OLS estimators.
#Econometrics
#MatrixForm
#Regression
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