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
Building Regression Models with SAS is an essential guide to learning about a variety of models that provide interpretability as well as predictive performance.
Sprache
Dateigröße
ISBN-13
978-1-951684-00-6 (9781951684006)
Schweitzer Klassifikation
Robert N. Rodriguez retired from SAS in 2018 after serving as a senior director in research and development. This book is based on his experience learning about the needs of SAS customers at user events and working with SAS/STAT developers to meet those needs. Bob received a PhD in statistics from the University of North Carolina at Chapel Hill and was a research statistician at General Motors Research Laboratories before joining SAS. He is a Fellow of the American Statistical Association and received the ASA Founders Award in 2009.
