Download e-book for iPad: Logistic Regression Using the SAS System: Theory and by Paul D. Allison

By Paul D. Allison

ISBN-10: 0471221759

ISBN-13: 9780471221753

Written in a casual and non-technical sort, this e-book first explains the idea at the back of logistic regression after which indicates tips to enforce it utilizing the SAS process. Allison comprises numerous designated, real-world examples of the social sciences to supply readers with a greater figuring out of the cloth. He additionally explores the variations and similarities among the generalizations of the logistic regression version.

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Under the AGGREGATE option, LOGISTIC forms the cross-classification of all levels of all the explanatory variables. " For each of those 19 profiles, the observed and expected frequencies for each of the two outcomes of the dependent variable are calculated based on the fitted model. The deviance is then computed as where Oj is the observed frequency and Ej is the expected frequency in cellj. In this example, the sum is taken over all 38 cells. The 15 degrees of freedom come from the 19 profiles minus the 4 estimated parameters in the model.

Failure to take such correlations into account can lead to seriously biased standard errors and test statistics. The GEE method—invoked by using the REPEATED statement—solves these problems in a convenient, flexible way. We'll discuss this method in some detail in Chapter 8. 28 • Logistic Regression Using the SAS System: Theory and Application What the GENMOD procedure lacks. Despite these desirable features, GENMOD lacks some important capabilities of PROC LOGISTIC that prevent it from being the universal logit procedure in SAS.

The parameter estimate is again very large, but the standard error is reported as 0 and chi-square and p-values are not reported. As with complete separation, the true maximum likelihood estimate for the coefficient of x is infinite. 0001 In my experience, the most common cause of quasi-complete separation is a dummy independent variable that has the following property: at one level of the dummy variable either every case has a 1 on the dependent variable or every case has a 0. To illustrate this, let's restrict the sample of death penalty cases to those with nonblack defendants.

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Logistic Regression Using the SAS System: Theory and Application by Paul D. Allison


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