Logistic Regression

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Logistic Regression


Logistic Regression
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Practical Guide To Logistic Regression


Practical Guide To Logistic Regression
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Author : Joseph M. Hilbe
language : en
Publisher: CRC Press
Release Date : 2016-04-05

Practical Guide To Logistic Regression written by Joseph M. Hilbe and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-05 with Mathematics categories.


Practical Guide to Logistic Regression covers the key points of the basic logistic regression model and illustrates how to use it properly to model a binary response variable. This powerful methodology can be used to analyze data from various fields, including medical and health outcomes research, business analytics and data science, ecology, fisheries, astronomy, transportation, insurance, economics, recreation, and sports. By harnessing the capabilities of the logistic model, analysts can better understand their data, make appropriate predictions and classifications, and determine the odds of one value of a predictor compared to another. Drawing on his many years of teaching logistic regression, using logistic-based models in research, and writing about the subject, Professor Hilbe focuses on the most important features of the logistic model. Serving as a guide between the author and readers, the book explains how to construct a logistic model, interpret coefficients and odds ratios, predict probabilities and their standard errors based on the model, and evaluate the model as to its fit. Using a variety of real data examples, mostly from health outcomes, the author offers a basic step-by-step guide to developing and interpreting observation and grouped logistic models as well as penalized and exact logistic regression. He also gives a step-by-step guide to modeling Bayesian logistic regression. R statistical software is used throughout the book to display the statistical models while SAS and Stata codes for all examples are included at the end of each chapter. The example code can be adapted to readers’ own analyses. All the code is available on the author’s website.

Logistic Regression


Logistic Regression
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Author : David G. Kleinbaum
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11

Logistic Regression written by David G. Kleinbaum and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Medical categories.


This text on logistic regression methods contains the following eight chapters: 1 Introduction to Logistic Regression 2 Important Special Cases of the Logistic Model 3 Computing the Odds Ratio in Logistic Regression 4 Maximum Likelihood Techniques: An Overview 5 Statistical Inferences Using Maximum Likelihood Techniques 6 Modeling Strategy Guidelines 7 Modeling Strategy for Assessing Interaction and Confounding 8 Analysis of Matched Data Using Logistic Regression Each chapter contains a presentation of its topic in "lecture-book" format together with objectives, an outline, key formulae, practice exercises, and a test. The "lecture-book" has a sequence of illustrations and formulae in the left column of each page and a script in the right column. This format allows you to read the script in conjunction with the illustrations and formulae that high light the main points, formulae, or examples being presented. The reader mayaiso purchase directly from the author audio-cassette tapes of each chapter. If you purchase the tapes, you may use the tape with the illustrations and formulae, ignoring the script. The use of the audiotape with the illustrations and formulae is intended to be similar to a lecture. An audio cassette player is the only equipment required. Tapes may be obtained by writing or calling the author at the following address: Depart ment of Epidemiology, School of Public Health, Emory University, 1599 Clifton Rd. N. E. , Atlanta, GA 30333, phone (404) 727-9667. This text is intended for self-study.

Applied Logistic Regression


Applied Logistic Regression
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Author : David W. Hosmer, Jr.
language : en
Publisher: John Wiley & Sons
Release Date : 2013-04-01

Applied Logistic Regression written by David W. Hosmer, Jr. and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-01 with Mathematics categories.


A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include: A chapter on the analysis of correlated outcome data A wealth of additional material for topics ranging from Bayesian methods to assessing model fit Rich data sets from real-world studies that demonstrate each method under discussion Detailed examples and interpretation of the presented results as well as exercises throughout Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines.

Logistic Regression Models


Logistic Regression Models
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Author : Joseph M. Hilbe
language : en
Publisher: CRC Press
Release Date : 2017-05-18

Logistic Regression Models written by Joseph M. Hilbe and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-18 with categories.


Logistic Regression Modelspresents an overview of the full range of logistic models, including binary, proportional, ordered, partially ordered, and unordered categorical response regression procedures. Other topics discussed include panel, survey, skewed, penalized, and exact logistic models. The text illustrates how to apply the various models to health, environmental, physical, and social science data. Examples illustrate successful modeling The text first provides basic terminology and concepts, before explaining the foremost methods of estimation (maximum likelihood and IRLS) appropriate for logistic models. It then presents an in-depth discussion of related terminology and examines logistic regression model development and interpretation of the results. After focusing on the construction and interpretation of various interactions, the author evaluates assumptions and goodness-of-fit tests that can be used for model assessment. He also covers binomial logistic regression, varieties of overdispersion, and a number of extensions to the basic binary and binomial logistic model. Both real and simulated data are used to explain and test the concepts involved. The appendices give an overview of marginal effects and discrete change as well as a 30-page tutorial on using Stata commands related to the examples used in the text. Stata is used for most examples while R is provided at the end of the chapters to replicate examples in the text. Apply the models to your own data Data files for examples and questions used in the text as well as code for user-authored commands are provided on the book's website, formatted in Stata, R, Excel, SAS, SPSS, and Limdep. See Professor Hilbe discuss the book.

Interpretable Machine Learning


Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2019

Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.




Logistic Regression


Logistic Regression
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Author : Scott Menard
language : en
Publisher: SAGE
Release Date : 2010

Logistic Regression written by Scott Menard and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Social Science categories.


Logistic Regression is designed for readers who have a background in statistics at least up to multiple linear regression, who want to analyze dichotomous, nominal, and ordinal dependent variables cross-sectionally and longitudinally.

Logistic Regression Using Sas


Logistic Regression Using Sas
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Author : Paul David Allison
language : en
Publisher: Sas Inst
Release Date : 2012

Logistic Regression Using Sas written by Paul David Allison and has been published by Sas Inst this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.


If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, Paul Allison's Logistic Regression Using SAS: Theory and Application, Second Edition, is for you! Informal and nontechnical, this book both explains the theory behind logistic regression, and looks at all the practical details involved in its implementation using SAS. Several real-world examples are included in full detail. This book also explains the differences and similarities among the many generalizations of the logistic regression model. The following topics are covered: binary logistic regression, logit analysis of contingency tables, multinomial logit analysis, ordered logit analysis, discrete-choice analysis, and Poisson regression. Other highlights include discussions on how to use the GENMOD procedure to do loglinear analysis and GEE estimation for longitudinal binary data. Only basic knowledge of the SAS DATA step is assumed. The second edition describes many new features of PROC LOGISTIC, including conditional logistic regression, exact logistic regression, generalized logit models, ROC curves, the ODDSRATIO statement (for analyzing interactions), and the EFFECTPLOT statement (for graphing nonlinear effects). Also new is coverage of PROC SURVEYLOGISTIC (for complex samples), PROC GLIMMIX (for generalized linear mixed models), PROC QLIM (for selection models and heterogeneous logit models), and PROC MDC (for advanced discrete choice models).SAS Products and Releases: SAS/STAT: 9.3_M1, 9.3, 9.22, 9.21_M1, 9.21, 9.2, 9.1.3, 9.1.2, 9.1, 9.0 Operating Systems: Windows