Basic Biostatistics for Geneticists andEpidemiologists - A Practical Approach

### English

Anyone who attempts to read genetics or epidemiology research literature needs to understand the essentials of biostatistics. This book, a revised new edition of the successful Essentials of Biostatistics has been written to provide such an understanding to those who have little or no statistical background and who need to keep abreast of new findings in this fast moving field. Unlike many other elementary books on biostatistics, the main focus of this book is to explain basic concepts needed to understand statistical procedures.

This Book:

• Surveys basic statistical methods used in the genetics and epidemiology literature, including maximum likelihood and least squares.
• Introduces methods, such as permutation testing and bootstrapping, that are becoming more widely used in both genetic and epidemiological research.
• Is illustrated throughout with simple examples to clarify the statistical methodology.
• Explains Bayes’ theorem pictorially.
• Features exercises, with answers to alternate questions, enabling use as a course text.

Written at an elementary mathematical level so that readers with high school mathematics will find the content accessible. Graduate students studying genetic epidemiology, researchers and practitioners from genetics, epidemiology, biology, medical research and statistics will find this an invaluable introduction to statistics.

### English

Robert C Elston, Professor Genetic and Molecular Epidemiology Track, Department of Epidemiology and Biostatistics, School of Medicine Case Western Reserve University, USA. An internationally prominent genetic epidemiologist with many years teaching experience.

William D Johnson, Medical Center, University of Mississippi, USA. An experienced human geneticist.

### English

Preface.

1. Introduction: The role and Relevance of Statistics, Genetics and Epidemiology In Medicine.

2. Populations, Samples, and Study Design.

3. Descriptive Statistics.

4. The Laws of Probability.

5. Random Variables and Distributions.

6. Estimates and Confidence Limits.

7. Significance Tests and Tests of Hypotheses.

8. Likelihood Ratios, Bayesian Methods and Multiple Hypotheses.

9. The Many Uses of Chi-Square.

10. Correlation and Regression.

11. Analysis of Variance and Linear Models.

12. Some Specialized Techniques.

13. Guides to a Critical Evaluation of Published Reports.

Epilogue.

Review Problems.