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rodriguez r building regression models sas guide data scient 2023

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Textbook in PDF format Advance your skills in building predictive models with SAS! Building Regression Models with SAS: A Guide for Data Scientists teaches data scientists, statisticians, and other analysts who use SAS to train regression models for prediction with large, complex data. Each chapter focuses on a particular model and includes a high-level overview, followed by basic concepts, essential syntax, and examples using new procedures in both SAS/STAT and SAS Viya. By emphasizing introductory examples and interpretation of output, this book provides readers with a clear understanding of how to build the following types of models: general linear models quantile regression models logistic regression models generalized linear models generalized additive models proportional hazards regression models tree models models based on multivariate adaptive regression splines Machine Learning has created a new divide for the practice of statistics, which relies heavily on data from well-designed studies for modeling and inference. Statistical methods now vie with algorithms that learn from large amounts of observational data. In particular, the new divide influences how regression models are viewed and applied. While statistical analysts view regression models as platforms for inference, data scientists view them as platforms for prediction. And while statistical analysts prefer to specify the effects in a model by drawing on subject matter knowledge, data scientists rely on algorithms to determine the form of the model. This book equips both groups to cross the divide and find value on the other side by presenting SAS procedures that build regression models for prediction from large numbers of candidate effects. It introduces statistical analysts to methods of predictive modeling drawn from supervised learning, and at the same time it introduces data scientists to a rich variety of models drawn from statistics. Building Regression Models with SAS is an essential guide to learning about a variety of models that provide interpretability as well as predictive performance. Knowledge Prerequisites for the Book: This book assumes you know the basics of regression analysis. It uses standard matrix notation for regression models but explains the concepts and methods behind the procedures without mathematical derivations. For readers who want to dive into the technical aspects of concepts and algorithms, explanations are given in appendices which use calculus and linear algebra at the level expected by master of science programs in Data Science and statistics. The book also assumes you know enough about SAS to write a program that reads data and runs procedures. Chapter 1. Introduction I General Linear Models Chapter 2. Building General Linear Models: Concepts Chapter 3. Building General Linear Models: Issues Chapter 4. Building General Linear Models: Methods Chapter 5. Building General Linear Models: Procedures Chapter 6. Building General Linear Models: Collinearity Chapter 7. Building General Linear Models: Model Averaging II Specialized Regression Models Chapter 8. Building Quantile Regression Models Chapter 9. Building Logistic Regression Models. Chapter 10. Building Generalized Linear Models Chapter 11. Building Generalized Additive Models Chapter 12. Building Proportional Hazards Models Chapter 13. Building Classification and Regression Trees Chapter 14. Building Adaptive Regression Models III Appendices about Algorithms and Computational Methods Appendix A. Algorithms for Least Squares Estimation Appendix B. Least Squares Geometry... 321 Appendix C. Akaike’s Information Criterion Appendix D. Maximum Likelihood Estimation for Generalized Linear Models Appendix E. Distributions for Generalized Linear Models Appendix F. Spline Methods Appendix G. Algorithms for Generalized Additive Models IV Appendices about Common Topics

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rodriguez r building regression models sas guide data scient 2023

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