Abstract
In much of the work presented in the last four chapters we have assumed that the Gauss-Markov conditions were true. We have also sometimes made the additional assumption that the errors and, therefore, the dependent variables were normally distributed. In practice, these assumptions do not always hold; in fact, quite often, at least one of them will be violated. In this and the next four chapters we shall examine how to check whether each of the assumptions actually holds and what, if anything, we can do if it does not. This chapter is devoted to the normality assumption.
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© 1990 Springer Science+Business Media New York
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Sen, A., Srivastava, M. (1990). The Normality Assumption. In: Regression Analysis. Springer Texts in Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-25092-1_5
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DOI: https://doi.org/10.1007/978-3-662-25092-1_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-97211-2
Online ISBN: 978-3-662-25092-1
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