This volume helps readers understand the intuitive logic behind logistic regression through nontechnical language and simple examples.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
FRED C. PAMPEL is Research Professor of Sociology and a Research Associate in the Population Program at the University of Colorado Boulder. He received a Ph.D. in sociology from the University of Illinois, Champaign-Urbana, in 1977, and has previously taught at the University of Iowa, University of North Carolina, and Florida State University. His research focuses on socioeconomic disparities in health behaviors, smoking in particular, and on the experimental and quasi-experimental methods for evaluation of social programs for youth. He is the author of several books on population aging, cohort change, and public policy, and his work has appeared in the American Sociological Review, the American Journal of Sociology, Demography, Social Forces, and the European Sociological Review.
Inhaltsangabe
Series Editor's Introduction Preface Acknowledgments About the Author Chapter 1: The Logic of Logistic Regression Regression With a Binary Dependent Variable Transforming Probabilities Into Logits Linearizing the Nonlinear Summary Chapter 2: Interpreting Logistic Regression Coefficients Logged Odds Odds Probabilities Standardized Coefficients Group and Model Comparisons of Logistic Regression Coefficients Summary Chapter 3: Estimation and Model Fit Maximum Likelihood Estimation Tests of Significance Using Log Likelihood Values Model Goodness of Fit Summary Chapter 4: Probit Analysis Another Way to Linearize the Nonlinear The Probit Transformation Interpretation Maximum Likelihood Estimation Summary Chapter 5: Ordinal and Multinomial Logistic Regression Ordinal Logistic Regression Multinomial Logistic Regression Summary Notes Appendix: Logarithms The Logic of Logarithms Properties of Logarithms Natural Logarithms Summary References Index
Series Editor's Introduction Preface Acknowledgments About the Author Chapter 1: The Logic of Logistic Regression Regression With a Binary Dependent Variable Transforming Probabilities Into Logits Linearizing the Nonlinear Summary Chapter 2: Interpreting Logistic Regression Coefficients Logged Odds Odds Probabilities Standardized Coefficients Group and Model Comparisons of Logistic Regression Coefficients Summary Chapter 3: Estimation and Model Fit Maximum Likelihood Estimation Tests of Significance Using Log Likelihood Values Model Goodness of Fit Summary Chapter 4: Probit Analysis Another Way to Linearize the Nonlinear The Probit Transformation Interpretation Maximum Likelihood Estimation Summary Chapter 5: Ordinal and Multinomial Logistic Regression Ordinal Logistic Regression Multinomial Logistic Regression Summary Notes Appendix: Logarithms The Logic of Logarithms Properties of Logarithms Natural Logarithms Summary References Index
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