Probability and Statistics for Engineering and the Sciences with Modeling using R

Probability and statistics courses are more popular than ever. Regardless of your major or your profession, you will most likely use concepts from probability and statistics often in your career. 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. The book focuses on engineering examples and applications, while also including social sciences and more examples. Depending on the chapter flows, a course can be tailored for students at all levels and background. Over many years of teaching this course, the authors created problems based on real data, student projects, and labs. Students have suggested these enhance their experience and learning. The authors hope to share projects and labs with other instructors and students to make the course more interesting for both. R is an excellent platform to use. This book uses R with real data sets. The labs can be used for group work, in class, or for self-directed study. These project labs have been class-tested for many years with good results and encourage students to apply the key concepts and use of technology to analyze and present results.

1. Introduction to Statistical Modeling and Models and R2. Introduction to Data3. Statistical Measures4. Classical Probability5. Discrete Distributions6. Continuous Probability Models7. Other Continuous Distribution (some calculus required): Triangular, Unnamed, Beta, Gamma8. Sampling Distributions9. Estimating Parameters10. One Sample Hypothesis Testing11. Two Sample Hypothesis Testing12. Reliability Modeling 13. Introduction to Regression Techniques14. Advanced Regression Models: Nonlinear, Sinusoidal, and Binary Logistics Regression using R15. ANOVA in R16. Two-way ANCOVA using R

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.