Ekstrom and Sorensen have revised their textbook based on comments, suggestions, and requests from lecturers and students. They have also added new chapters, one on non-linear regression models and another containing examples of complete data analyses to demonstrate how a full-fledged statistical analysis might be undertaken and the results presented. Other topics include linear regression, the normal distribution, hypothesis tests, model validation and prediction, probabilities, the binomial distribution. Students can use any statistical software, but the authors encourage the use of R. Annotation ©2015 Ringgold, Inc., Portland, OR (protoview.com)
A Hands-On Approach to Teaching Introductory Statistics
Expanded with over 100 more pages, Introduction to Statistical Data Analysis for the Life Sciences, Second Edition presents the right balance of data examples, statistical theory, and computing to teach introductory statistics to students in the life sciences. This popular textbook covers the mathematics underlying classical statistical analysis, the modeling aspects of statistical analysis and the biological interpretation of results, and the application of statistical software in analyzing real-world problems and datasets.
New to the Second Edition
- A new chapter on non-linear regression models
- A new chapter that contains examples of complete data analyses, illustrating how a full-fledged statistical analysis is undertaken
- Additional exercises in most chapters
- A summary of statistical formulas related to the specific designs used to teach the statistical concepts
This text provides a computational toolbox that enables students to analyze real datasets and gain the confidence and skills to undertake more sophisticated analyses. Although accessible with any statistical software, the text encourages a reliance on R. For those new to R, an introduction to the software is available in an appendix. The book also includes end-of-chapter exercises as well as an entire chapter of case exercises that help students apply their knowledge to larger datasets and learn more about approaches specific to the life sciences.
