Logistic Regression Inside and Out PDF Download

Author: Jeffrey Strickland
Publisher: Lulu.com
ISBN: 1365819159
Category :
Languages : en
Pages : 312

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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.

Author: Christoph Molnar
Publisher: Lulu.com
ISBN: 0244768528
Category : Artificial intelligence
Languages : en
Pages : 320

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Book Description
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: David G. Kleinbaum
Publisher: Springer Science & Business Media
ISBN: 1475741081
Category : Medical
Languages : en
Pages : 291

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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.

Author: Scott Menard
Publisher: SAGE
ISBN: 1412974836
Category : Mathematics
Languages : en
Pages : 393

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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.

Author: Fred C. Pampel
Publisher: SAGE
ISBN: 9780761920106
Category : Mathematics
Languages : en
Pages : 98

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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 logist

Author: Jason W. Osborne
Publisher: SAGE Publications
ISBN: 1483312097
Category : Social Science
Languages : en
Pages : 489

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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.

Author: Scott Menard
Publisher: SAGE Publications
ISBN: 1483351424
Category : Social Science
Languages : en
Pages : 393

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Book Description
In this text, author Scott Menard provides coverage of not only the basic logistic regression model but also advanced topics found in no other logistic regression text. The book keeps mathematical notation to a minimum, making it accessible to those with more limited statistics backgrounds, while including advanced topics of interest to more statistically sophisticated readers. Not dependent on any one software package, the book discusses limitations to existing software packages and ways to overcome them. Key Features Examines the logistic regression model in detail Illustrates concepts with applied examples to help readers understand how concepts are translated into the logistic regression model Helps readers make decisions about the criteria for evaluating logistic regression models through detailed coverage of how to assess overall models and individual predictors for categorical dependent variables Offers unique coverage of path analysis with logistic regression that shows readers how to examine both direct and indirect effects using logistic regression analysis Applies logistic regression analysis to longitudinal panel data, helping students understand the issues in measuring change with dichotomous, nominal, and ordinal dependent variables Shows readers how multilevel change models with logistic regression are different from multilevel growth curve models for continuous interval or ratio-scaled dependent variables Logistic Regression is intended for courses such as Regression and Correlation, Intermediate/Advanced Statistics, and Quantitative Methods taught in departments throughout the behavioral, health, mathematical, and social sciences, including applied mathematics/statistics, biostatistics, criminology/criminal justice, education, political science, public health/epidemiology, psychology, and sociology.

Author: David W. Hosmer, Jr.
Publisher: John Wiley & Sons
ISBN: 0471654027
Category : Mathematics
Languages : en
Pages : 397

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Book Description
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: Ronald Christensen
Publisher: Springer Science & Business Media
ISBN: 1475741111
Category : Mathematics
Languages : en
Pages : 420

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Book Description
This book examines log-linear models for contingency tables. Logistic re gression and logistic discrimination are treated as special cases and gener alized linear models (in the GLIM sense) are also discussed. The book is designed to fill a niche between basic introductory books such as Fienberg (1980) and Everitt (1977) and advanced books such as Bishop, Fienberg, and Holland (1975), Haberman (1974), and Santner and Duffy (1989). lt is primarily directed at advanced Masters degree students in Statistics but it can be used at both higher and lower levels. The primary theme of the book is using previous knowledge of analysis of variance and regression to motivate and explicate the use of log-linear models. Of course, both the analogies and the distinctions between the different methods must be kept in mind. The book is written at several levels. A basic introductory course would take material from Chapters I, II (deemphasizing Section II. 4), III, Sec tions IV. 1 through IV. 5 (eliminating the material on graphical models), Section IV. lü, Chapter VII, and Chapter IX. The advanced modeling ma terial at the end of Sections VII. 1, VII. 2, and possibly the material in Section IX. 2 should be deleted in a basic introductory course. For Mas ters degree students in Statistics, all the material in Chapters I through V, VII, IX, and X should be accessible. For an applied Ph. D.

Author: Werner Vach
Publisher: Springer Science & Business Media
ISBN: 1461226503
Category : Mathematics
Languages : en
Pages : 152

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Book Description
In many areas of science a basic task is to assess the influence of several factors on a quantity of interest. If this quantity is binary logistic, regression models provide a powerful tool for this purpose. This monograph presents an account of the use of logistic regression in the case where missing values in the variables prevent the use of standard techniques. Such situations occur frequently across a wide range of statistical applications. The emphasis of this book is on methods related to the classical maximum likelihood principle. The author reviews the essentials of logistic regression and discusses the variety of mechanisms which might cause missing values while the rest of the book covers the methods which may be used to deal with missing values and their effectiveness. Researchers across a range of disciplines and graduate students in statistics and biostatistics will find this a readable account of this.