jet-lag
发表于 2025-3-25 05:33:11
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
Phagocytes
发表于 2025-3-25 10:06:52
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
天然热喷泉
发表于 2025-3-25 13:06:19
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obeisance
发表于 2025-3-25 19:27:14
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
POINT
发表于 2025-3-25 23:10:18
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Gossamer
发表于 2025-3-26 00:58:48
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Melanocytes
发表于 2025-3-26 05:38:19
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
neologism
发表于 2025-3-26 11:10:14
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
Feedback
发表于 2025-3-26 13:53:39
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ALTER
发表于 2025-3-26 20:23:45
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