Building Regression Models with SAS A Guide for Data Scientists
Building Regression Models with SAS: A Guide for Data Scientists by Robert N. Rodriguez, published by SAS Institute on April 18, 2023, is a comprehensive resource for data scientists and statisticians looking to enhance their skills in predictive modeling using SAS. This edition spans 464 pages and is presented in English. The book focuses on training regression models for prediction with large, complex datasets, offering a structured approach to understanding various model types.
Readers will find detailed chapters that cover specific models, including general linear models, logistic regression models, and tree models, among others. Each chapter provides a high-level overview, essential syntax, and practical examples utilizing the latest procedures in SAS/STAT and SAS Viya. By emphasizing introductory examples and the interpretation of output, this guide aims to equip analysts with the knowledge needed to effectively build and understand a range of regression models, making it a valuable addition to the toolkit of anyone working with data analysis.
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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.
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