Titlebook: Regression Analysis; Theory, Methods and Ashish Sen,Muni Srivastava Book 1990 Springer Science+Business Media New York 1990 Fitting.Random

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Multicollinearity, closely related to each other. This situation, which (with a slight abuse of language) is called ., is the subject of this chapter and is also the underlying factor that motivates the methods treated in Chapters 11 and 12.

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Indicator Variables,ainly be concerned with using them as independent variables. In general, their use as dependent variables in least squares analysis is not recommended. Nonetheless, we shall consider the topic further in the final section of this chapter.

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The Normality Assumption,ld; 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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Variable Selection,cing the number of variables often reduces multicollinearity. Still another reason is that it lowers the ratio of the number of variables to the number of observations, which is beneficial in many ways.

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