The primary goal behind this book is offering the flexibility for instructors to build most undergraduate courses upon it. This book is designed for either a one-semester course in either introductory probability and statistics (not calculus-based) and/or a one-semester course in a calculus-based probability and statistics course.

Dr. William P. Fox is a visiting professor of Computational Operations Research in the Mathematics Department at the College of William and Mary. He is an emeritus professor in the Department of Defense Analysis at the Naval Postgraduate School. He earned his BS degree from the United States Military Academy, MS in operations research from the Naval Postgraduate School, and his PhD in Industrial Engineering from Clemson University. He has taught at the United States Military Academy and at Francis Marion University. He has many publications and scholarly activities including 16 books, 21 book chapters and technical reports, 150 journal articles, and more than 150 conference presentations and mathematical modeling workshops.

Rodney X. Sturdivant, PhD, is director of the Statistical Consulting Center and an associate professor in the Department of Statistical Science at Baylor University. He has been senior research biostatistician with the Henry M. Jackson Foundation for the Advancement of Military Medicine supporting the Uniformed Services University of Health Science. Previously, he was professor of Applied Statistics at Azusa Pacific University. He was associate professor of Clinical Public Health in the Biostatistics Division of the College of Public Health at The Ohio State University. He retired as a Colonel after 27-year career in the U.S. Army. He completed his military service as an Academy Professor and Professor of Applied Statistics in the Department of Mathematical Sciences at the United States Military Academy, West Point. He earned a B.S. from West Point, an M.S. in statistics and an M.S. in operations research from Stanford, and a PhD in biostatistics from the University of Massachusetts - Amherst.

1. Introduction to Statistical Modeling and Models and R. 2. Introduction to Data. 3. Statistical Measures. 4. Classical Probability. 5. Discrete Distributions. 6. Continuous Probability Models. 7. Other Continuous Distribution (some calculus required): Triangular, Unnamed, Beta, Gamma. 8. Sampling Distributions. 9. Estimating Parameters. 10. One Sample Hypothesis Testing. 11. Two Sample Hypothesis Testing. 12. Reliability Modeling. 13. Introduction to Regression Techniques. 14. Advanced Regression Models: Nonlinear, Sinusoidal, and Binary Logistics Regression using R. 15. ANOVA in R. 16. Two-way ANCOVA using R.