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Handbook for applied modeling : non-Gaussian and correlated data

Author: Jamie Riggs; Trent Lalonde
Publisher: Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2017. ©2017
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Fulltext
Internet Resources
Additional Physical Format: Print version:
Riggs, Jamie.
Handbook for applied modeling.
Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2017
(DLC) 2017004641
(OCoLC)971248493
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Jamie Riggs; Trent Lalonde
ISBN: 9781316544778 131654477X
OCLC Number: 1001514409
Description: 1 online resource : illustrations
Contents: The data sets --
The model-building process --
Constant variance response models --
Nonconstant variance response models --
Discrete, categorical response models --
Count response models --
Time-to-event response models --
Longitudinal response models --
Structural equation modeling --
Matching data to models.
Responsibility: Jamie D. Riggs, Northwestern University, Illinois; Trent L. Lalonde, University of Northern Colorado, Colorado.

Abstract:

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  Read more...

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'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 Read more...

 
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