Building Regression Models with SAS: A Guide for Data Scientists

Автор: literator от 28-05-2023, 19:09, Коментариев: 0

Категория: КНИГИ » ОС И БД

Building Regression Models with SAS: A Guide for Data ScientistsНазвание: Building Regression Models with SAS: A Guide for Data Scientists
Автор: Robert N. Rodriguez
Издательство: SAS Institute Inc.
Год: 2023
Страниц: 464
Язык: английский
Формат: pdf (true)
Размер: 17.7 MB

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.

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