This new edition presents the essential topics in probability and statistics from a rigorous standpoint. Any discipline involving randomness, including medicine, engineering, and any area of scientific research, must have a way of analyzing or even predicting the outcomes of an experiment. The authors focus on the tools for doing so in a thorough, yet introductory way. After providing an overview of the basics of probability, the authors cover essential topics such as confidence intervals, hypothesis testing, and linear regression. These subjects are presented in a one semester format, suitable for engineers, scientists, and STEM students with a solid understanding of calculus. There are problems and exercises included in each chapter allowing readers to practice the applications of the concepts.

About the Author

Emmanuel N. Barron, Ph.D., is an Emeritus Professor of Mathematics and Statistics at Loyola University Chicago. After receiving his B.S. in mathematics from the University of Illinois at Chicago, he earned his M.S. and Ph.D. in mathematics from Northwestern University. While at Northwestern University, he specialized in partial differential equations and differential games. He also held positions as an Assistant Professor at Georgia Tech and a Member of Technical Staff at Bell Laboratories. Dr. Barron has published over 80 research papers and two books.

John G. DelGreco, Ph.D., is an Associate Professor of Mathematics and Statistics at Loyola University Chicago. He holds a B.S. in mathematics from John Carroll University, an M.A. in mathematics from the University of Massachusetts, and a Ph.D. in industrial engineering from Purdue University. Before joining Loyola's faculty in 1987, Dr. DelGreco worked as a systems analyst for Micro Data Base Systems, Inc., located in Lafayette, Indiana. His research interests include applied graph theory, operations research, network flows, and parallel algorithms.

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