By Stanley Lemeshow, David W. Hosmer Jr., Rodney X. Sturdivant

ISBN-10: 0470582472

ISBN-13: 9780470582473

 A new version of the definitive consultant to logistic regression modeling for health and wellbeing technology and different applications
This completely multiplied Third variation provides an simply obtainable creation to the logistic regression (LR) version and highlights the ability of this version through reading the connection among a dichotomous final result and a collection of covariables.

Applied Logistic Regression, 3rd version emphasizes purposes within the healthiness sciences and handpicks themes that top go well with using sleek statistical software program. The booklet presents readers with cutting-edge options for development, analyzing, and assessing the functionality of LR types. New and up-to-date good points include:
• A bankruptcy at the research of correlated end result data
• A wealth of extra fabric for themes starting from Bayesian the way to assessing version fit
• wealthy info units from real-world reports that show every one approach less than discussion
• certain examples and interpretation of the awarded effects in addition to routines throughout

Applied Logistic Regression, 3rd variation is a must have advisor for execs and researchers who have to version nominal or ordinal scaled end result variables in public overall healthiness, drugs, and the social sciences in addition to a variety of different fields and disciplines.

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Additional resources for Applied Logistic Regression (Wiley Series in Probability and Statistics)

Example text

We defer a detailed discussion of the interpretation of these results to Chapter 3. 158 with 95 percent confidence. As is the case with any regression model, the constant term provides an estimate of the response at x = 0 unless the independent variable has been centered at some clinically meaningful value. In our example, the constant provides an estimate of the log-odds ratio of CHD at zero years of age. As a result, the constant term, by itself, has no useful clinical interpretation. 087). The logit is the linear part of the logistic regression model and, as such, is most similar to the fitted line in a linear regression model.

These data are used here to illustrate methods when prediction is the final goal as well as to demonstrate various model building techniques. 9 and the data are referred to as the BURN1000 data. g Was the subject’s father myopic? 75D. measure of the eye’s effective focusing power. 00 D. The more negative the spherical equivalent, the more myopic the subject. c The length of eye from front to back. d The length from front to back of the aqueous-containing space of the eye between the cornea and the iris.

As is the case with any regression model, the constant term provides an estimate of the response at x = 0 unless the independent variable has been centered at some clinically meaningful value. In our example, the constant provides an estimate of the log-odds ratio of CHD at zero years of age. As a result, the constant term, by itself, has no useful clinical interpretation. 087). The logit is the linear part of the logistic regression model and, as such, is most similar to the fitted line in a linear regression model.

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Applied Logistic Regression (Wiley Series in Probability and Statistics) by Stanley Lemeshow, David W. Hosmer Jr., Rodney X. Sturdivant


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