Statistical Methods For The Analysis Of Biomedical Data, Second Edition

The new edition adds a chapter on multiple linear regression in biomedical research, with sections including the multiple linear regressions model and least squares; the ANOVA table, parameter estimates, and confidence intervals; partial f-tests; polynomial regression; and analysis of covariance.
* Organized by problem rather than method, so it guides readers to the correct technique for solving the problem at hand.

ROBERT F. WOOLSON, PhD, is a Professor of Biostatistics and Associate Dean for Research at the University of Iowa College of Public Health.

WILLIAM R. CLARKE, PhD, is a Professor of Biostatistics at the University of Iowa College of Public Health.

Preface to the 1987 Edition.

Preface to the 2002 Edition.

Acknowledgments.

1. Introduction.

2. Descriptive Statistics.

3. Basic Probability Concepts.

4. Further Aspects of Probability for Statistical Inference: Sampling, Probability Distributions, and Sampling Disctributions.

5. Confidence Intervals and Hypothesis Testing: General Considerations and Applications.

6. Comparison of Two Groups: t-Tests and Rank Tests.

7. Comparison of Two Groups: Chi-Square and Related Procedures.

8. Tests of Independence and Measure of Association for Two Random Variables.

9. Least-Square Regression Methods: Predicting One Variable from Another.

10. Comparing More Than Two Groups of Observations: Analysis of Variance for Comparing Groups.

11. Comparing More Than Two Groups of Observations: Rank Analysis of Variance for Group Comparisons.

12. Comparing More than Two Groups of Observations: Chi-Square and Related Procedures.

13. Special Topics in Analysis of Epidemiologic and Clinical Data: Studying Association Between a Disease and a Characteristic.

14. Estimation and Comparison of Survival Curves.

15. Multiple Linear Regression Methods: Predicting One Variable from Two or More Other Variables.

Appendix.