Logistic Regression Models for Ordinal Response Variables

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Author :
Release : 2006
Genre : Mathematics
Kind :
Book Rating : 895/5 ( reviews)

Logistic Regression Models for Ordinal Response Variables - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Logistic Regression Models for Ordinal Response Variables write by Ann A. O'Connell. This book was released on 2006. Logistic Regression Models for Ordinal Response Variables available in PDF, EPUB and Kindle. Ordinal measures provide a simple and convenient way to distinguish among possible outcomes. The book provides practical guidance on using ordinal outcome models.

Logistic Regression Models for Ordinal Response Variables

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Author :
Release : 2005-11-02
Genre : Social Science
Kind :
Book Rating : 837/5 ( reviews)

Logistic Regression Models for Ordinal Response Variables - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Logistic Regression Models for Ordinal Response Variables write by Ann A. O′Connell. This book was released on 2005-11-02. Logistic Regression Models for Ordinal Response Variables available in PDF, EPUB and Kindle. Logistic Regression Models for Ordinal Response Variables provides applied researchers in the social, educational, and behavioral sciences with an accessible and comprehensive coverage of analyses for ordinal outcomes. The content builds on a review of logistic regression, and extends to details of the cumulative (proportional) odds, continuation ratio, and adjacent category models for ordinal data. Description and examples of partial proportional odds models are also provided. This book is highly readable, with lots of examples and in-depth explanations and interpretations of model characteristics. SPSS and SAS are used for all examples; data and syntax are available from the author′s website. The examples are drawn from an educational context, but applications to other fields of inquiry are noted, such as HIV prevention, behavior change, counseling psychology, social psychology, etc.). The level of the book is set for applied researchers who need to quickly understand the use and application of these kinds of ordinal regression models.

Handbook of Regression Modeling in People Analytics

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Release : 2021-07-29
Genre : Business & Economics
Kind :
Book Rating : 897/5 ( reviews)

Handbook of Regression Modeling in People Analytics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Handbook of Regression Modeling in People Analytics write by Keith McNulty. This book was released on 2021-07-29. Handbook of Regression Modeling in People Analytics available in PDF, EPUB and Kindle. Despite the recent rapid growth in machine learning and predictive analytics, many of the statistical questions that are faced by researchers and practitioners still involve explaining why something is happening. Regression analysis is the best ‘swiss army knife’ we have for answering these kinds of questions. This book is a learning resource on inferential statistics and regression analysis. It teaches how to do a wide range of statistical analyses in both R and in Python, ranging from simple hypothesis testing to advanced multivariate modelling. Although it is primarily focused on examples related to the analysis of people and talent, the methods easily transfer to any discipline. The book hits a ‘sweet spot’ where there is just enough mathematical theory to support a strong understanding of the methods, but with a step-by-step guide and easily reproducible examples and code, so that the methods can be put into practice immediately. This makes the book accessible to a wide readership, from public and private sector analysts and practitioners to students and researchers. Key Features: 16 accompanying datasets across a wide range of contexts (e.g. academic, corporate, sports, marketing) Clear step-by-step instructions on executing the analyses Clear guidance on how to interpret results Primary instruction in R but added sections for Python coders Discussion exercises and data exercises for each of the main chapters Final chapter of practice material and datasets ideal for class homework or project work.

Analysis of Ordinal Categorical Data

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Release : 2012-07-06
Genre : Mathematics
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Book Rating : 990/5 ( reviews)

Analysis of Ordinal Categorical Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Analysis of Ordinal Categorical Data write by Alan Agresti. This book was released on 2012-07-06. Analysis of Ordinal Categorical Data available in PDF, EPUB and Kindle. Statistical science’s first coordinated manual of methods for analyzing ordered categorical data, now fully revised and updated, continues to present applications and case studies in fields as diverse as sociology, public health, ecology, marketing, and pharmacy. Analysis of Ordinal Categorical Data, Second Edition provides an introduction to basic descriptive and inferential methods for categorical data, giving thorough coverage of new developments and recent methods. Special emphasis is placed on interpretation and application of methods including an integrated comparison of the available strategies for analyzing ordinal data. Practitioners of statistics in government, industry (particularly pharmaceutical), and academia will want this new edition.

Logistic Regression

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Author :
Release : 2010
Genre : Mathematics
Kind :
Book Rating : 836/5 ( reviews)

Logistic Regression - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Logistic Regression write by Scott W. Menard. This book was released on 2010. Logistic Regression available in PDF, EPUB and Kindle. 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.