Handbook for Applied Modeling: Non-Gaussian and Correlated Data

Nonfiction, Science & Nature, Mathematics, Statistics, Social & Cultural Studies, Social Science
Cover of the book Handbook for Applied Modeling: Non-Gaussian and Correlated Data by Jamie D. Riggs, Trent L. Lalonde, Cambridge University Press
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Author: Jamie D. Riggs, Trent L. Lalonde ISBN: 9781108206914
Publisher: Cambridge University Press Publication: July 14, 2017
Imprint: Cambridge University Press Language: English
Author: Jamie D. Riggs, Trent L. Lalonde
ISBN: 9781108206914
Publisher: Cambridge University Press
Publication: July 14, 2017
Imprint: Cambridge University Press
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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