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. Ronald D. Fricker is Vice Provost for Faculty Affairs at Virginia Tech, where he has served as head of the Department of Statistics, Senior Associate Dean in the College of Science and, subsequently, interim dean of the college. He is the author of Introduction to Statistical Methods for Biosurveillance (2013) and with Steve Rigdon, Monitoring the Health of Populations by Tracking Disease Outbreaks (2020). He is a fellow of the American Statistical Association, a fellow of the American Association for the Advancement of Science, and an elected member of the Virginia Academy of Science, Engineering, and Medicine. Douglas C. Montgomery is Regents Professor and ASU Foundation Professor of Engineering at Arizona State University. He is an Honorary Member of the American Society for Quality, a fellow of the American Statistical Association, a fellow of the Institute of Industrial and Systems Engineering, and a fellow of the Royal Statistical Society. He is the author of fifteen other books including Design and Analysis of Experiments, 10th edition (2013) and Design of Experiments for Reliability Achievement (2022). He has received the Shewhart Medal, the Distinguished Service Medal, and the Brumbaugh Award from the ASQ, the Deming Lecture Award from the ASA, the Greenfield Medal from the Royal Statistical Society, and the George Box Medal from the European Network for Business and Industrial Statistics.

'This book serves as an excellent resource for students with diverse backgrounds, offering a thorough exploration of fundamental topics in statistics. The clear explanation of concepts, methods, and theory, coupled with an abundance of practical examples, provides a solid foundation to help students understand statistical principles and bridge the gap between theory and application. This book offers invaluable insights and guidance for anyone seeking to master the principles of statistics. I highly recommend adopting this book for my future statistics class.' Haijun Gong, Saint Louis University

• 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.

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. Ronald D. Fricker is Vice Provost for Faculty Affairs at Virginia Tech, where he has served as head of the Department of Statistics, Senior Associate Dean in the College of Science and, subsequently, interim dean of the college. He is the author of Introduction to Statistical Methods for Biosurveillance (2013) and with Steve Rigdon, Monitoring the Health of Populations by Tracking Disease Outbreaks (2020). He is a fellow of the American Statistical Association, a fellow of the American Association for the Advancement of Science, and an elected member of the Virginia Academy of Science, Engineering, and Medicine. Douglas C. Montgomery is Regents Professor and ASU Foundation Professor of Engineering at Arizona State University. He is an Honorary Member of the American Society for Quality, a fellow of the American Statistical Association, a fellow of the Institute of Industrial and Systems Engineering, and a fellow of the Royal Statistical Society. He is the author of fifteen other books including Design and Analysis of Experiments, 10th edition (2013) and Design of Experiments for Reliability Achievement (2022). He has received the Shewhart Medal, the Distinguished Service Medal, and the Brumbaugh Award from the ASQ, the Deming Lecture Award from the ASA, the Greenfield Medal from the Royal Statistical Society, and the George Box Medal from the European Network for Business and Industrial Statistics.

'This book serves as an excellent resource for students with diverse backgrounds, offering a thorough exploration of fundamental topics in statistics. The clear explanation of concepts, methods, and theory, coupled with an abundance of practical examples, provides a solid foundation to help students understand statistical principles and bridge the gap between theory and application. This book offers invaluable insights and guidance for anyone seeking to master the principles of statistics. I highly recommend adopting this book for my future statistics class.' Haijun Gong, Saint Louis University

• 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.

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. Ronald D. Fricker is Vice Provost for Faculty Affairs at Virginia Tech, where he has served as head of the Department of Statistics, Senior Associate Dean in the College of Science and, subsequently, interim dean of the college. He is the author of Introduction to Statistical Methods for Biosurveillance (2013) and with Steve Rigdon, Monitoring the Health of Populations by Tracking Disease Outbreaks (2020). He is a fellow of the American Statistical Association, a fellow of the American Association for the Advancement of Science, and an elected member of the Virginia Academy of Science, Engineering, and Medicine. Douglas C. Montgomery is Regents Professor and ASU Foundation Professor of Engineering at Arizona State University. He is an Honorary Member of the American Society for Quality, a fellow of the American Statistical Association, a fellow of the Institute of Industrial and Systems Engineering, and a fellow of the Royal Statistical Society. He is the author of fifteen other books including Design and Analysis of Experiments, 10th edition (2013) and Design of Experiments for Reliability Achievement (2022). He has received the Shewhart Medal, the Distinguished Service Medal, and the Brumbaugh Award from the ASQ, the Deming Lecture Award from the ASA, the Greenfield Medal from the Royal Statistical Society, and the George Box Medal from the European Network for Business and Industrial Statistics.

'This book serves as an excellent resource for students with diverse backgrounds, offering a thorough exploration of fundamental topics in statistics. The clear explanation of concepts, methods, and theory, coupled with an abundance of practical examples, provides a solid foundation to help students understand statistical principles and bridge the gap between theory and application. This book offers invaluable insights and guidance for anyone seeking to master the principles of statistics. I highly recommend adopting this book for my future statistics class.' Haijun Gong, Saint Louis University

• 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.