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Author: David W. Hosmer, Jr. Publisher: Wiley-Interscience ISBN: 9780471208266 Category : Mathematics Languages : en Pages : 280
Book Description
Presenting information on logistic regression models, this work explains difficult concepts through illustrative examples. This is a solutions manual to accompany applied Logistic Regression, 2nd Edition.
Author: David W. Hosmer, Jr. Publisher: Wiley-Interscience ISBN: 9780471208266 Category : Mathematics Languages : en Pages : 280
Book Description
Presenting information on logistic regression models, this work explains difficult concepts through illustrative examples. This is a solutions manual to accompany applied Logistic Regression, 2nd Edition.
Author: David W. Hosmer, Jr. Publisher: John Wiley & Sons ISBN: 0471654027 Category : Mathematics Languages : en Pages : 397
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: David W. Hosmer, Jr. Publisher: Wiley-Interscience ISBN: 9780471208266 Category : Mathematics Languages : en Pages : 280
Book Description
Presenting information on logistic regression models, this work explains difficult concepts through illustrative examples. This is a solutions manual to accompany applied Logistic Regression, 2nd Edition.
Author: David W. Hosmer, Jr. Publisher: Wiley-Interscience ISBN: 9780471225898 Category : Mathematics Languages : en Pages : 0
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.
Author: Iain Pardoe Publisher: John Wiley & Sons ISBN: 1118345045 Category : Mathematics Languages : en Pages : 319
Book Description
Praise for the First Edition "The attention to detail is impressive. The book is very well written and the author is extremely careful with his descriptions . . . the examples are wonderful." —The American Statistician Fully revised to reflect the latest methodologies and emerging applications, Applied Regression Modeling, Second Edition continues to highlight the benefits of statistical methods, specifically regression analysis and modeling, for understanding, analyzing, and interpreting multivariate data in business, science, and social science applications. The author utilizes a bounty of real-life examples, case studies, illustrations, and graphics to introduce readers to the world of regression analysis using various software packages, including R, SPSS, Minitab, SAS, JMP, and S-PLUS. In a clear and careful writing style, the book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, and Bayesian modeling. In addition, the Second Edition features clarification and expansion of challenging topics, such as: Transformations, indicator variables, and interaction Testing model assumptions Nonconstant variance Autocorrelation Variable selection methods Model building and graphical interpretation Throughout the book, datasets and examples have been updated and additional problems are included at the end of each chapter, allowing readers to test their comprehension of the presented material. In addition, a related website features the book's datasets, presentation slides, detailed statistical software instructions, and learning resources including additional problems and instructional videos. With an intuitive approach that is not heavy on mathematical detail, Applied Regression Modeling, Second Edition is an excellent book for courses on statistical regression analysis at the upper-undergraduate and graduate level. The book also serves as a valuable resource for professionals and researchers who utilize statistical methods for decision-making in their everyday work.
Author: Sanford Weisberg Publisher: John Wiley & Sons ISBN: 1118625951 Category : Mathematics Languages : en Pages : 266
Book Description
Master linear regression techniques with a new edition of a classic text Reviews of the Second Edition: "I found it enjoyable reading and so full of interesting material that even the well-informed reader will probably find something new . . . a necessity for all of those who do linear regression." —Technometrics, February 1987 "Overall, I feel that the book is a valuable addition to the now considerable list of texts on applied linear regression. It should be a strong contender as the leading text for a first serious course in regression analysis." —American Scientist, May–June 1987 Applied Linear Regression, Third Edition has been thoroughly updated to help students master the theory and applications of linear regression modeling. Focusing on model building, assessing fit and reliability, and drawing conclusions, the text demonstrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. To facilitate quick learning, the Third Edition stresses the use of graphical methods in an effort to find appropriate models and to better understand them. In that spirit, most analyses and homework problems use graphs for the discovery of structure as well as for the summarization of results. The Third Edition incorporates new material reflecting the latest advances, including: Use of smoothers to summarize a scatterplot Box-Cox and graphical methods for selecting transformations Use of the delta method for inference about complex combinations of parameters Computationally intensive methods and simulation, including the bootstrap method Expanded chapters on nonlinear and logistic regression Completely revised chapters on multiple regression, diagnostics, and generalizations of regression Readers will also find helpful pedagogical tools and learning aids, including: More than 100 exercises, most based on interesting real-world data Web primers demonstrating how to use standard statistical packages, including R, S-Plus®, SPSS®, SAS®, and JMP®, to work all the examples and exercises in the text A free online library for R and S-Plus that makes the methods discussed in the book easy to use With its focus on graphical methods and analysis, coupled with many practical examples and exercises, this is an excellent textbook for upper-level undergraduates and graduate students, who will quickly learn how to use linear regression analysis techniques to solve and gain insight into real-life problems.
Author: Vijay K. Rohatgi Publisher: John Wiley & Sons ISBN: 1118165683 Category : Mathematics Languages : en Pages : 747
Book Description
The second edition of a well-received book that was published 24 years ago and continues to sell to this day, An Introduction to Probability and Statistics is now revised to incorporate new information as well as substantial updates of existing material.
Author: Jirí Andel Publisher: John Wiley & Sons ISBN: 0470317914 Category : Mathematics Languages : en Pages : 272
Book Description
Mathematics of Chance utilizes simple, real-world problems-some of which have only recently been solved-to explain fundamental probability theorems, methods, and statistical reasoning. Jiri Andel begins with a basic introduction to probability theory and its important points before moving on to more specific sections on vital aspects of probability, using both classic and modern problems. Each chapter begins with easy, realistic examples before covering the general formulations and mathematical treatments used. The reader will find ample use for a chapter devoted to matrix games and problem sets concerning waiting, probability calculations, expectation calculations, and statistical methods. A special chapter utilizes problems that relate to areas of mathematics outside of statistics and considers certain mathematical concepts from a probabilistic point of view. Sections and problems cover topics including: * Random walks * Principle of reflection * Probabilistic aspects of records * Geometric distribution * Optimization * The LAD method, and more Knowledge of the basic elements of calculus will be sufficient in understanding most of the material presented here, and little knowledge of pure statistics is required. Jiri Andel has produced a compact reference for applied statisticians working in industry and the social and technical sciences, and a book that suits the needs of students seeking a fundamental understanding of probability theory.
Author: S. James Press Publisher: John Wiley & Sons ISBN: 1118150627 Category : Mathematics Languages : en Pages : 296
Book Description
Comparing and contrasting the reality of subjectivity in the workof history's great scientists and the modern Bayesian approach tostatistical analysis Scientists and researchers are taught to analyze their data from anobjective point of view, allowing the data to speak for themselvesrather than assigning them meaning based on expectations oropinions. But scientists have never behaved fully objectively.Throughout history, some of our greatest scientific minds haverelied on intuition, hunches, and personal beliefs to make sense ofempirical data-and these subjective influences have often aided inhumanity's greatest scientific achievements. The authors argue thatsubjectivity has not only played a significant role in theadvancement of science, but that science will advance more rapidlyif the modern methods of Bayesian statistical analysis replace someof the classical twentieth-century methods that have traditionallybeen taught. To accomplish this goal, the authors examine the lives and work ofhistory's great scientists and show that even the most successfulhave sometimes misrepresented findings or been influenced by theirown preconceived notions of religion, metaphysics, and the occult,or the personal beliefs of their mentors. Contrary to popularbelief, our greatest scientific thinkers approached their data witha combination of subjectivity and empiricism, and thus informallyachieved what is more formally accomplished by the modern Bayesianapproach to data analysis. Yet we are still taught that science is purely objective. Thisinnovative book dispels that myth using historical accounts andbiographical sketches of more than a dozen great scientists,including Aristotle, Galileo Galilei, Johannes Kepler, WilliamHarvey, Sir Isaac Newton, Antoine Levoisier, Alexander vonHumboldt, Michael Faraday, Charles Darwin, Louis Pasteur, GregorMendel, Sigmund Freud, Marie Curie, Robert Millikan, AlbertEinstein, Sir Cyril Burt, and Margaret Mead. Also included is adetailed treatment of the modern Bayesian approach to dataanalysis. Up-to-date references to the Bayesian theoretical andapplied literature, as well as reference lists of the primarysources of the principal works of all the scientists discussed,round out this comprehensive treatment of the subject. Readers will benefit from this cogent and enlightening view of thehistory of subjectivity in science and the authors' alternativevision of how the Bayesian approach should be used to further thecause of science and learning well into the twenty-first century.
Author: David W. Hosmer, Jr.
Author: David W. Hosmer, Jr.
Author: David W. Hosmer, Jr.
Author: David W. Hosmer, Jr.
Author: David W. Hosmer, Jr.
Author: David W. Hosmer, Jr.
Author: Iain Pardoe
Author: Sanford Weisberg
Author: Vijay K. Rohatgi
Author: Jirí Andel
Author: S. James Press