Building Regression Models With Sas


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Building Regression Models with SAS


Building Regression Models with SAS

Author: Robert N. Rodriguez

language: en

Publisher: SAS Institute

Release Date: 2023-04-18


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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.

Building Better Models with JMP Pro


Building Better Models with JMP Pro

Author: Jim Grayson

language: en

Publisher: SAS Institute

Release Date: 2015-08


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Explore the black box of business analytics and learn the methodology for managing and executing analytics projects.

Simulating Data with SAS


Simulating Data with SAS

Author: Rick Wicklin

language: en

Publisher: SAS Institute

Release Date: 2013-04-22


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Data simulation is a fundamental technique in statistical programming and research. Rick Wicklin's Simulating Data with SAS brings together the most useful algorithms and the best programming techniques for efficient data simulation in an accessible how-to book for practicing statisticians and statistical programmers. This book discusses in detail how to simulate data from common univariate and multivariate distributions, and how to use simulation to evaluate statistical techniques. It also covers simulating correlated data, data for regression models, spatial data, and data with given moments. It provides tips and techniques for beginning programmers, and offers libraries of functions for advanced practitioners. As the first book devoted to simulating data across a range of statistical applications, Simulating Data with SAS is an essential tool for programmers, analysts, researchers, and students who use SAS software. This book is part of the SAS Press program.