Applied Logistic Regression (Second Edition)

一本很好的关于逻辑回归分析的书 Review "...The book is a classic, extremely well written, and it includes a variety of software packages and real examples...." -- The Statistician, Vol. 51, No.2, 2002 "...an excellent book that balances many objectives well.... All statistical practitioners...can benefit from this book...Applied Logistic Regression is an ideal choice." -- Technometrics, February 2002 "...it remains an extremely valuable text for everyone working or teaching in fields like epidemiology..." -- Stat istics in Medicine, No.21, 2002 "...the revised text continues to provide a focused introduction to the logistic regression model and its use in methods for modelling..." -- Short Book Reviews, Vol. 21, No. 2, August 2001 "In this revised and updated edition of the popular test, the authors incorporate theoretical and computing advances from the last decade." -- Journal of the American Statistical Association, September 2001 Product 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. istics in Medicine, No.21, 2002 "...the revised text continues to provide a focused introduction to the logistic regression model and its use in methods for modelling..." -- Short Book Reviews, Vol. 21, No. 2, August 2001 "In this revised and updated edition of the popular test, the authors incorporate theoretical and computing advances from the last decade." -- Journal of the American Statistical Association, September 2001 Product 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.