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 the book 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.
Table of Contents:
Description of Samples and Populations
Data types
Visualizing categorical data
Visualizing quantitative data
Statistical summaries
What is a probability?
Linear Regression
Fitting a regression line
When is linear regression appropriate?
The correlation coefficient
Perspective
Comparison of Groups
Graphical and simple numerical comparison
Between-group variation and within-group variation
Populations, samples, and expected values
Least squares estimation and residuals
Paired and unpaired samples
Perspective
The Normal Distribution
Properties
One sample
Are the data (approximately) normally distributed?
The central limit theorem
Statistical Models, Estimation, and Confidence Intervals
Statistical models
Estimation
Confidence intervals
Unpaired samples with different standard deviations
Hypothesis Tests
Null hypotheses
t-tests
Tests in a one-way ANOVA
Hypothesis tests as comparison of nested models
Type I and type II errors
Model Validation and Prediction
Model validation
Prediction
Linear Normal Models
Multiple linear regression
Additive two-way analysis of variance
Linear models
Interactions between variables
Probabilities
Outcomes, events, and probabilities
Conditional probabilities
Independence
The Binomial Distribution
The independent trials model
The binomial distribution
Estimation, confidence intervals, and hypothesis tests
Differences between proportions
Analysis of Count Data
The chi-square test for goodness-of-fit
2 × 2 contingency table
Two-sided contingency tables
Logistic Regression
Odds and odds ratios
Logistic regression models
Estimation and confidence intervals
Hypothesis tests
Model validation and prediction
Case Exercises
Case 1: Linear modeling
Case 2: Data transformations
Case 3: Two sample comparisons
Case 4: Linear regression with and without intercept
Case 5: Analysis of variance and test for linear trend
Case 6: Regression modeling and transformations
Case 7: Linear models
Case 8: Binary variables
Case 9: Agreement
Case 10: Logistic regression
Appendix A: Summary of Inference Methods
Statistical concepts
Statistical analysis
Model selection
Appendix B: Introduction to R
Working with R
Data frames and reading data into R
Manipulating data
Graphics with R
Reproducible research
Installing R
Exercises
Appendix C: Statistical Tables
The x2 distribution
The normal distribution
The t distribution
The F distribution
Bibliography
Index
R Commands and Output and Exercises appear at the end of each chapter.
About the Author :
Claus Thorn Ekstrom 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. His research interests include genetic marker error detection, simulation-based inference, image analysis, and the analysis of microarray DNA chips, metabolic profiles, and quantitative traits for complex human families. Helle Sorensen 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. Her research interests include statistical applications in eco-toxicology and animal science as well as statistical methods for stochastic processes.
Review :
"This book is a delightful addition to introductory statistical texts … an excellent textbook that mixes a reasonable level of mathematical rigor with focus on practical applications and computational statistics."
—Journal of Agricultural, Biological, and Environmental Statistics, Volume 17, Number 4, 2012
"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
