Applied Categorical Data Analysis
The nonstatistician's quick reference to applied categorical data analysis
With a succinct, unified approach to applied categorical data analysis and an emphasis on applications, this book is immensely useful to researchers and students in the biomedical disciplines and to anyone concerned with statistical analysis. This self-contained volume provides up-to-date coverage of all major methodologies in this area of applied statistics and acquaints the reader with statistical thinking as expressed through a variety of modern-day topics and techniques. Applied Categorical Data Analysis introduces a number of new research areas, including the Mantel-Haenszel method, Kappa statistics, ordinal risks, odds ratio estimates, goodness-of-fit, and various regression models for categorical data.
Chap T. Le, author of Health and Numbers and Applied Survival Analysis, presents his information in a user-friendly format and an accessible style while purposefully keeping the mathematics to a level appropriate for students in applied fields. Well supplemented with helpful graphs and tables, Applied Categorical Data Analysis:
* Covers both basic and advanced topics
* Employs many real-life examples from biomedicine, epidemiology, and public health
* Presents case studies in meticulous detail
* Provides end-of-chapter exercise sets and solutions
* Incorporates samples of computer programs (most notably in SAS).
Applied Categorical Data Analysis is an important resource for graduate students and professionals who need a compact reference and guide to both the fundamentals and applications of the major methods in the field.
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Logistic Regression Models
Methods for Matched Data
5 other sections not shown
95 percent confidence Acid Applications associated binary binomial case-control study categorical data cell chi-square test conditional independence confidence interval confounder consider controls covariates data set death degrees of freedom disease distribution effect modifications endometrial cancer Error z Statistic estimate explanatory exposed exposure goodness-of-fit group of variables include these instructions independent variables individual interaction terms interest investigation likelihood function likelihood ratio chi-square linear logistic model logistic regression logistic regression model loglinear model lung cancer Mantel-Haenszel method matched set measure multiple regression model Multivariate null hypothesis observed obtained odds ratio outcome overdispersion p-Value parameters patients percent confidence interval Poisson distribution Poisson regression population prediction probability PROC procedure program would include proportional hazards model Refer regression analysis relationship relative risk risk factor sample SAS program Second Edition significant smoking specific standard error Statistic p-Value step stepwise survival analysis tion versus Xray
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