Katherine E. Irimata is a mathematical statistician at the National Center for Health Statistics* in the Division of Research and Methodology. Brittany N. Dugger is an assistant professor of pathology and laboratory medicine at the University of California, Davis, where she is the neuropathology core coleader at the Alzheimer's Disease Center. Jeffrey R. Wilson is an associate professor of statistics and biostatistics at Arizona State University. He is the coauthor of Modeling Binary Correlated Responses using SAS, SPSS and R and the coeditor of Innovative Statistical Methods for Public Health Data. *Katherine E. Irimata is serving in her personal capacity, and the work for this book was initiated and conducted when she was a student at Arizona State University. The views expressed are those of the authors and do not necessarily represent the views of the National Center for Health Statistics, the Centers for Disease Control and Prevention, or the US government.

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• 1. Introduction to Statistical Software and Alzheimer's Data2. Review of Introductory Statistical Methods3. Generalized Linear Models4. Hierarchical Regression Models for Continuous Responses5. Hierarchical Logistic Regression Models6. Bayesian Regression Models7. Multiple Membership Models8. Survival Data Analysis9. Modeling Responses with Time-dependent Covariates10. Joint Modeling of Mean and Dispersion11. Neural Networks and Other Machine Learning Techniques for Big Data12. Case StudyReferencesAcknowledgments

Katherine E. Irimata is a mathematical statistician at the National Center for Health Statistics* in the Division of Research and Methodology. Brittany N. Dugger is an assistant professor of pathology and laboratory medicine at the University of California, Davis, where she is the neuropathology core coleader at the Alzheimer's Disease Center. Jeffrey R. Wilson is an associate professor of statistics and biostatistics at Arizona State University. He is the coauthor of Modeling Binary Correlated Responses using SAS, SPSS and R and the coeditor of Innovative Statistical Methods for Public Health Data. *Katherine E. Irimata is serving in her personal capacity, and the work for this book was initiated and conducted when she was a student at Arizona State University. The views expressed are those of the authors and do not necessarily represent the views of the National Center for Health Statistics, the Centers for Disease Control and Prevention, or the US government.

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• 1. Introduction to Statistical Software and Alzheimer's Data2. Review of Introductory Statistical Methods3. Generalized Linear Models4. Hierarchical Regression Models for Continuous Responses5. Hierarchical Logistic Regression Models6. Bayesian Regression Models7. Multiple Membership Models8. Survival Data Analysis9. Modeling Responses with Time-dependent Covariates10. Joint Modeling of Mean and Dispersion11. Neural Networks and Other Machine Learning Techniques for Big Data12. Case StudyReferencesAcknowledgments

Katherine E. Irimata is a mathematical statistician at the National Center for Health Statistics* in the Division of Research and Methodology. Brittany N. Dugger is an assistant professor of pathology and laboratory medicine at the University of California, Davis, where she is the neuropathology core coleader at the Alzheimer's Disease Center. Jeffrey R. Wilson is an associate professor of statistics and biostatistics at Arizona State University. He is the coauthor of Modeling Binary Correlated Responses using SAS, SPSS and R and the coeditor of Innovative Statistical Methods for Public Health Data. *Katherine E. Irimata is serving in her personal capacity, and the work for this book was initiated and conducted when she was a student at Arizona State University. The views expressed are those of the authors and do not necessarily represent the views of the National Center for Health Statistics, the Centers for Disease Control and Prevention, or the US government.

Select Guide Rating

• 1. Introduction to Statistical Software and Alzheimer's Data2. Review of Introductory Statistical Methods3. Generalized Linear Models4. Hierarchical Regression Models for Continuous Responses5. Hierarchical Logistic Regression Models6. Bayesian Regression Models7. Multiple Membership Models8. Survival Data Analysis9. Modeling Responses with Time-dependent Covariates10. Joint Modeling of Mean and Dispersion11. Neural Networks and Other Machine Learning Techniques for Big Data12. Case StudyReferencesAcknowledgments