This textbook is designed for students in statistics, data science, biostatistics, engineering, and physical science programs who need a solid course in the fundamental concepts, methods and theory of statistics to understand, use, and build on modern statistical techniques for complex problems. Examples and exercises incorporate data and R code.

Steven E. Rigdon is Professor of Biostatistics at Saint Louis University. He is a fellow of the American Statistical Association and is the author of Statistical Methods for the Reliability of Repairable Systems Calculus, 8th and 9th editions, Monitoring the Health of Populations by Tracking Disease Outbreaks (2020), and Design of Experiments for Reliability Achievement (2022). He has received the Waldo Vizeau Award for technical contributions to quality, the Soren Bisgaard Award, and the Paul Simon Award for linking teaching and research. He is also Distinguished Research Professor Emeritus at Southern Illinois University Edwardsville.

Part I. Descriptive Statistics & Data Science: 1. Introduction; 2. Descriptive statistics; 3. Data visualization; Part II. Probability: 4. Basic probability; 5. Random variables; 6. Discrete distributions; 7. Continuous distribution; Part III. Classical Statistical Inference: 8. About data & data collection; 9. Sampling distributions; 10. Point estimation; 11. Confidence intervals; 12. Hypothesis testing; 13. Hypothesis tests for two or more samples; 14. Hypothesis tests for discrete data; 15. Regression; Part IV. Bayesian and Other Computer Intensive Methods: 16. Bayesian methods; 17. Time series methods; 18. The jackknife and bootstrap; Part V. Advanced Topics in Inference & Data Science: 19. Generalized linear models and regression trees; 20. Cross-validation and estimates of prediction error; 21. Large-scale hypothesis testing and the false discovery rate; Appendix. More About R.