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Direct linkCai, Li; Hayes, Andrew F. – Journal of Educational and Behavioral Statistics, 2008
When the errors in an ordinary least squares (OLS) regression model are heteroscedastic, hypothesis tests involving the regression coefficients can have Type I error rates that are far from the nominal significance level. Asymptotically, this problem can be rectified with the use of a heteroscedasticity-consistent covariance matrix (HCCM)…
Descriptors: Least Squares Statistics, Error Patterns, Error Correction, Computation
