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Primer of applied regression and analysis of variance 3rd Edition

Name: Primer of applied regression and analysis of variance 3rd Edition
SKU: EB-12082820
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Primer of applied regression and analysis of variance 3rd Edition Glantz S.A. Digital Instant Download

Author(s): Glantz S.A., Slinker B.K., Neilands T.B

ISBN(s): 9780071822442, 9780071824118, 0071822445, 0071824111

Edition: 3

File Details: PDF, 26.69 MB

Year: 2016

Language: English

SKU: EB-12082820 Category: Science (General) Tags: applied regression, Bryan Slinker, Stanton Glantz, Torsten Neilands

Description

Primer of applied regression and analysis of variance 3rd Edition- Ebook Instant Download/Delivery ISBN(s): 9780071822442,9780071824118,0071822445,0071824111

Product details:

ISBN 10: 0071822445
ISBN 13: 9780071822442
Author: Stanton Glantz , Bryan Slinker, Torsten Neilands

Primer of Applied Regression & Analysis of Variance is a textbook especially created for medical, public health, and social and environmental science students who need applied (not theoretical) training in the use of statistical methods. The book has been acclaimed for its user-friendly style that makes complicated material understandable to readers who do not have an extensive math background.

The text is packed with learning aids that include chapter-ending summaries and end-of-chapter problems that quickly assess mastery of the material. Examples from biological and health sciences are included to clarify and illustrate key points. The techniques discussed apply to a wide range of disciplines, including social and behavioral science as well as health and life sciences. Typical courses that would use this text include those that cover multiple linear regression and ANOVA.

Table contents:

Chapter One: Why Do Multivariate Analysis?
Chapter Two: The First Step: Understanding Simple Linear Regression
Chapter Three: Regression with Two or More Independent Variables
Chapter Four: Do the Data Fit the Assumptions?
Chapter Five: Multicollinearity and What to Do About It
Chapter Six: Selecting the “Best” Regression Model
Chapter Seven: Missing Data
Chapter Eight: One-Way Analysis of Variance
Chapter Nine: Two-Way Analysis of Variance
Chapter Ten: Repeated Measures
Chapter Eleven: Mixing Continuous and Categorical Variables: Analysis of Covariance
Chapter Twelve: Regression with a Qualitative Dependent Variable: Logistic Regression
Chapter Thirteen: Regression Modeling of Time-to-Event Data: Survival Analysis
Chapter Fourteen: Nonlinear Regression

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