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.
... MATHIOWETZ, and SUDMAN - Measurement Errors in Surveys COCHRAN - Sampling Techniques, Third Edition COUPER, BAKER, BETHLEHEM, CLARK, MARTIN, NICHOLLS, and O'REILLY (editors) - Computer Assisted Survey Information Collection COX, ...
The focus in this Second Edition is again on logistic regression models for individual level data, but aggregate or grouped data are also considered.
Other methods for exploring additive interaction have been considered in the literature including most popularly the Relative Excess Risk due to Interaction or RERI [Rothman et al. (2008); Hosmer and Lemeshow (1992)].
... Noel A. C. Cressie, Garrett M. Fitzmaurice, Harvey Goldstein, Iain M. Johnstone, Geert Molenberghs, David W. Scott, Adrian F. M. Smith, Ruey S. Tsay, Sanford Weisberg Editors Emeriti: Vic Barnett, J. Stuart Hunter, Joseph B. Kadane, ...
Rothman,K.J. and Greenland,S. (1998). Modern Epidemiology.3rd edition. LippincottRaven., Philadelphia, PA. Royston, P(2001). Flexible parametric alternatives to the Coxmodeland more. TheStata Journal, 1(1 ): 1–28. Royston, P.(2004).
Emphasizing the parallels between linear and logistic regression, Scott Menard explores logistic regression analysis and demonstrates its usefulness in analyzing dichotomous, polytomous nominal, and polytomous ordinal dependent variables. The book...
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.
... Indicators Sullivan/Feldman Exploratory Data Analysis Hartwig/Dearing Reliability and Validity Assessment Carmines/Zeller Analyzing Panel Data Markus Discriminant Analysis Klecka Log-Linear Models Knoke/Burke Interrupted Time Series ...
Glenview, IL: Scott, Foresman. Lipsey, M. W. (1998). Design sensitivity: Statistical power for applied experimental research. In L. Bickman and D. J. Rog (eds.), Handbook of Applied Social Research Methods. Thousand Oaks, CA: Sage.
Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book.