Handbook for Applied Modeling: Non-Gaussian and Correlated Data - Softcover

Riggs, Jamie D.; Lalonde, Trent L.

Softcover

ISBN 10: 1316601056 ISBN 13: 9781316601051

Publisher: Cambridge University Press, 2017

Synopsis

Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing non-Gaussian and correlated data. Many practitioners work with data that fail the assumptions of the common linear regression models, necessitating more advanced modeling techniques. This Handbook presents clearly explained modeling options for such situations, along with extensive example data analyses. The book explains core models such as logistic regression, count regression, longitudinal regression, survival analysis, and structural equation modelling without relying on mathematical derivations. All data analyses are performed on real and publicly available data sets, which are revisited multiple times to show differing results using various modeling options. Common pitfalls, data issues, and interpretation of model results are also addressed. Programs in both R and SAS are made available for all results presented in the text so that readers can emulate and adapt analyses for their own data analysis needs. Data, R, and SAS scripts can be found online at http://www.spesi.org.

About this title

Review:

'This book is a guide to modeling and analyzing non-Gaussian and correlated data. There is clearly a need for such a book to help less experienced data scientists ... The data sets and models are well explained, and the limitations of each type of model on the various data sets is illustrated by frequent plots.' Peter Rabinovitch, MAA Reviews

Book Description:

Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing data that fail idealized assumptions. It explains and demonstrates core techniques, common pitfalls and data issues, and interpretation of model results, all with a focus on application, utility, and real-life data.

About this edition

• Publisher: Cambridge University Press
• Publication date: 2017
• ISBN 10: 1316601056
• ISBN 13: 9781316601051
• Binding: Paperback
• Number of pages: 228