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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Multiple Regression,rst to simple regression, which we considered in Chapter 1, and then to multiple regression. After that we shall derive formulae for least squares estimates and present properties of these estimates. These properties will be derived under the Gauss-Markov conditions which were presented in Chapter 1

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Indicator Variables,. We have already encountered such variables in Example 2.2, p. 31. They have a wide range of uses which will be discussed in this chapter. We shall mainly be concerned with using them as independent variables. In general, their use as dependent variables in least squares analysis is not recommended

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Unequal Variances,re. These conditions can be checked and if we find that one or more of them are seriously violated, we can take action that will cause at least approximate compliance. This and the next few chapters will deal with various ways in which these G-M conditions can be violated and what we would then need

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Multicollinearity,ditions. These conditions only assure us that least squares estimates will be ‘best’ for a given set of independent variables; i.e., for a given . matrix. Unfortunately, the quality of estimates, as measured by their variances, can be seriously and adversely affected if the independent variables are

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Variable Selection,easons we would like to cull the list. One important reason is the resultant parsimony: It is easier to work with simpler models. Another is that reducing the number of variables often reduces multicollinearity. Still another reason is that it lowers the ratio of the number of variables to the numbe

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