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 is aimed at readers with a background in bivariate and multiple linear regression.
... 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, ...
... 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, ...
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.
From the reviews of the First Edition.
'Tis better to use a logit or probit link function than to inappropriately use ordinary least squares regression with binary or categorical dependent variables . . .” —attributed to Warren Shakespeare, William's younger statistician ...
The first book to provide a unified framework for both single-level and multilevel modeling of ordinal categorical data, Applied Ordinal Logistic Regression Using Stata helps readers learn how to conduct analyses, interpret the results from ...
Ordinal measures provide a simple and convenient way to distinguish among possible outcomes. The book provides practical guidance on using ordinal outcome models.
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).
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.
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.