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Any practical introduction to statistics in the life sciences requires a focus on applications and computational statistics combined with a reasonable level of mathematical rigor. It must offer the right combination of data examples, statistical theory, and computing required for analysis today. And it should involve R software, the lingua franca of statistical computing.
Introduction to Statistical Data Analysis for the Life Sciences covers all the usual material but goes further than other texts to emphasize:
- Both data analysis and the mathematics underlying classical statistical analysis
- Modeling aspects of statistical analysis with added focus on biological interpretations
- Applications of statistical software in analyzing real-world problems and data sets
Developed from their courses at the University of Copenhagen, the authors imbue readers with the ability to model and analyze data early in the text and then gradually fill in the blanks with needed probability and statistics theory. While the main text can be used with any statistical software, the authors encourage a reliance on R. They provide a short tutorial for those new to the software and include R commands and output at the end of each chapter. Data sets used in Introduction to Statistical Data Analysis for the Life Sciences are available on a supporting website.
Each chapter contains a number of exercises, half of which can be done by hand. The text also contains ten case exercises where readers are encouraged to apply their knowledge to larger data sets and learn more about approaches specific to the life sciences. Ultimately, readers come away with a computational toolbox that enables them to perform actual analysis for real data sets as well as the confidence and skills to undertake more sophisticated analyses as their careers progress.
Description of Samples and Populations
Linear Regression
Comparison of Groups
The Normal Distribution
Statistical Models, Estimation, and Confidence Intervals
Hypothesis Tests
Model Validation and Prediction
Linear Normal Models
Probabilities
The Binomial Distribution
Analysis of Count Data
Logistic Regression
Case Exercises
Appendix A: Summary of Inference Methods
Appendix B: Introduction to R
Appendix C: Statistical Tables
Bibliography
Index
Claus Thorn Ekstrøm is an associate professor of statistics in the Department of Basic Sciences and Environment and leader of the Center for Applied Bioinformatics in the Faculty of Life Sciences at the University of Copenhagen.
Helle Sørensen is an associate professor of statistics and probability theory in the Department of Mathematical Sciences in the Faculty of Science at the University of Copenhagen.
"This book can be valuable assistance for students of life sciences and the other biological faculties and it can be treated both as a first handbook to statistical methods as well as a suitable tool to systematize earlier experiences. [...] The book is written in a clear and engaging style. The authors put much emphasis on the modelling part of statistical analysis and on biological interpretation of obtained results. It could be recommended for students but also other readers looking for a handbook of 'practical' statistics."
– Ewa Skotarczak, International Statistical Review, 2012
