Logistic Regression


Book Description

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


Book Description

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.




Logistic Regression Inside and Out


Book Description

If you have a yes or no question, then you can probably answer it with a logistic regression model. Logistic regression is most appropriate when the dependent variable has two possible outcomes. Will customers respond to an offer or unsubscribe, will the enemy fight or flee, will subjects respond to treatment or grow ill, will livestock live or die? Yes or no? I am often asked if logistic regression is a machine learning algorithm. I say that it is not, for I can formulate it mathematically and solve it using matrix equations, for example. Its solution is derived deterministically, and estimation is performed mathematically, through optimization methods. The logit link functionis the mathematical expression-a nonlinear, exponential equation, and we transform it to a linear equation by applying the natural logarithm. Here we find mathematical modeling, probability, and statistics. Here I will take you on a journey into the art and science of predictive modeling using logistic regression, inside-and-out.




Logistic Regression Models


Book Description

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




Logistic Regression


Book Description

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 logistic regression through the use of careful explanations and worked out examples. Pampel first offers readers a review of some basic concepts, such as exponents, percentage change, and likelihood functions. Next, he describes in some detail how taking the log of the odds eliminates the floor so that the transformation of logistic regression coefficients into coefficients that effect odds and probabilities makes more sense to readers. And, third, he describes maximum likelihood estimation through words and simple samples (along side of the formulas) so as to make the concept more concrete and the procedure easier to comprehend. Throughout the book, he emphasizes examples, explanations, and how to interpret the results of each procedure. This book will enable readers to use and understand logistic regression techniques and will serve as a foundation for more advanced treatments of the topic. Learn more about "The Little Green Book" - QASS Series! Click Here




Practical Guide to Logistic Regression


Book Description

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, fishe




Best Practices in Logistic Regression


Book Description

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. Best Practices in Logistic Regression explains logistic regression in a concise and simple manner that gives students the clarity they need without the extra weight of longer, high-level texts.







Logistic Regression Models for Ordinal Response Variables


Book Description

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




Interpreting and Comparing Effects in Logistic, Probit and Logit Regression


Book Description

Interpreting and Comparing Effects in Logistic, Probit and Logit Regression shows applied researchers how to compare coefficient estimates from regression models for categorical dependent variables in typical research situations. It presents a practical, unified treatment of these problems, and considers the advantages and disadvantages of each approach, and when to use them.