Abstract
This chapter deals with predicting a categorical response variable that has two categories. The chapter begins with using a single independent variable to make this prediction. It moves on to discuss the case where there are two categorical independent variables. Next comes a discussion of making predictions with a mixture of quantitative and categorical independent variables. Finally, adjusted odds ratios are considered followed by testing for an interaction effect between the independent variables.
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Data Sets and References
Coronary Heart Disease.sav obtained from: Hosmer, D.W., Lemeshow, S.: Applied Logistic Regression. Wiley, New York (1989). (With the kind permission of Professors David W. Hosmer and Stanley Lemeshow)
Diabetes.sav obtained from: Cassel, S., Mahoney, G., Troia, L., Volles, A., Henry, N.J., Holmes, W.H.: Prevalence of Risk Factors for Type 2 Diabetes Mellitus in a Population Served by a Health Clinic for the Uninsured. Unpublished data, Le Moyne College, Syracuse, New York (2010)
ICU.sav obtained from: Hosmer, D.W., Lemeshow, S.: Applied Logistic Regression. Wiley, New York (1989). (With the Kind Permission of John Wiley and Sons, and Professors David W. Hosmer and Stanley Lemeshow)
Barker, S., Jerome, J., Woods, D., Zaika, C., Brown, R.G., Holmes, W.H.: The Sit and Reach Test as a Measure of Flexibility for Predicting Lower Extremity Injury in Division III Athletes. Unpublished data, Le Moyne College, Syracuse, New York (2010)
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© 2014 Springer International Publishing Switzerland
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Holmes, W., Rinaman, W. (2014). Logistic Regression. In: Statistical Literacy for Clinical Practitioners. Springer, Cham. https://doi.org/10.1007/978-3-319-12550-3_15
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DOI: https://doi.org/10.1007/978-3-319-12550-3_15
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Online ISBN: 978-3-319-12550-3
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