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Regression

Classical Assumptions

TermDefinition
Classical Assumption #1 The regression model is linear, is correctly specified, and has an additive error term.
Classical Assumption #2 The error term has a zero population mean.
Classical Assumption #3 All explanatory variables are uncorrelated with the error term.
Classical Assumption #4 Observations of the error term are uncorrelated with each other. (no serial correlation)
Classical Assumption #5 The error term has a constant variance. (no heteroskedasticity)
Classical Assumption #6 No explanatory variable is a perfect linear function of any other explanatory variable(s). (no perfect multicollinearity)
Classical Assumption #7 The error term is normally distributed. (this assumption is optional but usually is invoked)
Created by: kristel387
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