Logistic Regression

Author : David G. Kleinbaum
Publisher : Springer Science & Business Media
Page : 291 pages
File Size : 54,8 Mb
Release : 2013-11-11
Category : Medical
ISBN : 9781475741087

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Logistic Regression by David G. Kleinbaum Pdf

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.

Author : David W. Hosmer, Jr.,Stanley Lemeshow
Publisher : John Wiley & Sons
Page : 397 pages
File Size : 55,5 Mb
Release : 2004-10-28
Category : Mathematics
ISBN : 9780471654025

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Applied Logistic Regression by David W. Hosmer, Jr.,Stanley Lemeshow Pdf

From the reviews of the First Edition. "An interesting, useful, and well-written book on logistic regression models . . . Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references." —Choice "Well written, clearly organized, and comprehensive . . . the authors carefully walk the reader through the estimation of interpretation of coefficients from a wide variety of logistic regression models . . . their careful explication of the quantitative re-expression of coefficients from these various models is excellent." —Contemporary Sociology "An extremely well-written book that will certainly prove an invaluable acquisition to the practicing statistician who finds other literature on analysis of discrete data hard to follow or heavily theoretical." —The Statistician In this revised and updated edition of their popular book, David Hosmer and Stanley Lemeshow continue to provide an amazingly accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets. Hosmer and Lemeshow extend the discussion from biostatistics and epidemiology to cutting-edge applications in data mining and machine learning, guiding readers step-by-step through the use of modeling techniques for dichotomous data in diverse fields. Ample new topics and expanded discussions of existing material are accompanied by a wealth of real-world examples-with extensive data sets available over the Internet.

Author : Christoph Molnar
Publisher : Lulu.com
Page : 320 pages
File Size : 41,5 Mb
Release : 2020
Category : Artificial intelligence
ISBN : 9780244768522

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Interpretable Machine Learning by Christoph Molnar Pdf

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Author : Scott Menard
Publisher : SAGE
Page : 393 pages
File Size : 43,9 Mb
Release : 2010
Category : Mathematics
ISBN : 9781412974837

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Logistic Regression by Scott Menard Pdf

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.

Author : Jason W. Osborne
Publisher : SAGE Publications
Page : 488 pages
File Size : 41,9 Mb
Release : 2014-02-26
Category : Social Science
ISBN : 9781483323138

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Best Practices in Logistic Regression by Jason W. Osborne Pdf

Jason W. Osborne’s Best Practices in Logistic Regression provides students with an accessible, applied approach that communicates logistic regression in clear and concise terms. The book effectively leverages readers’ basic intuitive understanding of simple and multiple regression to guide them into a sophisticated mastery of logistic regression. Osborne’s applied approach offers students and instructors a clear perspective, elucidated through practical and engaging tools that encourage student comprehension.

Author : Fred C. Pampel
Publisher : SAGE
Page : 98 pages
File Size : 54,5 Mb
Release : 2000-05-26
Category : Mathematics
ISBN : 0761920102

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Logistic Regression by Fred C. Pampel Pdf

Trying to determine when to use a logistic regression and how to interpret the coefficients? Frustrated by the technical writing in other books on the topic? Pampel's book offers readers the first "nuts and bolts" approach to doing logist

Author : Joseph M. Hilbe
Publisher : CRC Press
Page : 658 pages
File Size : 48,8 Mb
Release : 2009-05-11
Category : Mathematics
ISBN : 9781420075779

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Logistic Regression Models by Joseph M. Hilbe Pdf

Logistic Regression Models presents 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 t

Author : Joseph M. Hilbe
Publisher : CRC Press
Page : 174 pages
File Size : 54,6 Mb
Release : 2016-04-05
Category : Mathematics
ISBN : 9781498709583

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Practical Guide to Logistic Regression by Joseph M. Hilbe Pdf

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.

Author : James Jaccard
Publisher : SAGE Publications
Page : 80 pages
File Size : 51,5 Mb
Release : 2001-02-21
Category : Social Science
ISBN : 9781544332598

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Interaction Effects in Logistic Regression by James Jaccard Pdf

Oriented toward the applied researcher with a basic background in multiple regression and logistic regression, this book shows readers the general strategies for testing interactions in logistic regression as well as providing the tools to interpret and understand the meaning of coefficients in equations with product terms. Using completely worked-out examples, the author focuses on the interpretation of the coefficients of interactive logistic models for a wide range of scenarios encountered in the research literature. In addition, the author avoids complex formulas in favor of simple computer-based heuristics that permit the simple calculation of parameter estimates and estimated standard errors that will typically be of interest to applied researchers.

Author : Frank E. Harrell
Publisher : Springer Science & Business Media
Page : 583 pages
File Size : 44,8 Mb
Release : 2013-03-09
Category : Mathematics
ISBN : 9781475734621

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Regression Modeling Strategies by Frank E. Harrell Pdf

Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".

Author : Keith McNulty
Publisher : CRC Press
Page : 272 pages
File Size : 42,9 Mb
Release : 2021-07-29
Category : Business & Economics
ISBN : 9781000427899

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Handbook of Regression Modeling in People Analytics by Keith McNulty Pdf

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.

Author : Trevor Hastie,Robert Tibshirani,Jerome Friedman
Publisher : Springer Science & Business Media
Page : 545 pages
File Size : 51,6 Mb
Release : 2013-11-11
Category : Mathematics
ISBN : 9780387216065

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The Elements of Statistical Learning by Trevor Hastie,Robert Tibshirani,Jerome Friedman Pdf

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

Author : Ronald Christensen
Publisher : Springer Science & Business Media
Page : 484 pages
File Size : 46,8 Mb
Release : 2006-04-06
Category : Mathematics
ISBN : 9780387226248

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Log-Linear Models and Logistic Regression by Ronald Christensen Pdf

The primary focus here is on log-linear models for contingency tables, but in this second edition, greater emphasis has been placed on logistic regression. The book explores topics such as logistic discrimination and generalised linear models, and builds upon the relationships between these basic models for continuous data and the analogous log-linear and logistic regression models for discrete data. It also carefully examines the differences in model interpretations and evaluations that occur due to the discrete nature of the data. Sample commands are given for analyses in SAS, BMFP, and GLIM, while numerous data sets from fields as diverse as engineering, education, sociology, and medicine are used to illustrate procedures and provide exercises. Throughoutthe book, the treatment is designed for students with prior knowledge of analysis of variance and regression.

Author : Dan Jurafsky
Publisher : Pearson Education India
Page : 912 pages
File Size : 53,8 Mb
Release : 2000-09
Category : Electronic
ISBN : 8131716724

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Speech & Language Processing by Dan Jurafsky Pdf

Author : Ann A. O'Connell
Publisher : SAGE
Page : 124 pages
File Size : 55,7 Mb
Release : 2006
Category : Mathematics
ISBN : 0761929894

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Logistic Regression Models for Ordinal Response Variables by Ann A. O'Connell Pdf

Ordinal measures provide a simple and convenient way to distinguish among possible outcomes. The book provides practical guidance on using ordinal outcome models.