Titlebook: Logistic Regression; A Self-Learning Text David G. Kleinbaum Textbook 19941st edition Springer Science+Business Media New York 1994 class.d

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Logistic Regression978-1-4757-4108-7Series ISSN 1431-8776 Series E-ISSN 2197-5671

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Important Special Cases of the Logistic Model,ding odds ratio expressions. In particular, focus is on defining the independent variables that go into the model and on computing the odds ratio for each special case. Models that account for the potential confounding effects and potential interaction effects of covariates are emphasized.

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Maximum Likelihood Techniques: An Overview, We also distinguish between two alternative ML methods, called the unconditional and the conditional approaches, and we give guidelines regarding how the applied user can choose between these methods. Finally, we provide a brief overview of how to make statistical inferences using ML estimates.

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https://doi.org/10.1007/978-1-4757-4108-7class; design; likelihood; logistic regression; presentation; statistical inference; tool

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