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An overview of the classical linear regression model, focusing on population and fitted sample expressions. Topics covered include y observation, y mean, deviation from mean, errors, disturbance, residual, error mean, first and second normal equations, homoscedasticity, mean square residual, and cov(error,x).
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sociology classical linear regression model
population expressions fitted sample expressions
Y observation
Y mean
deviation from mean (errors)
disturbance residual
error mean
(1st normal equation)
error variance
disturbance variance hom oscedastic
mean square residual
Cov (error,x)
(2nd^ normal equation)
