Probability & Statistics with R for Engineers and Scientists

Description

Hallmark features of this title

• Major concepts are introduced early. This engages students immediately and helps them see the big picture, which then sets an appropriate tone for the course. In subsequent chapters, these topics are revisited, developed, and formalized, but this early introduction helps students build a true understanding of the concepts.
• Regression is introduced early (Chapter 4) and is used the rest of the text.
• Chapter 4 deals with joint (mainly bivariate) distributions, covers the standard topics (marginal and conditional distributions, and independence of random variables), but also introduces the important concepts of covariance and correlation, along with the notion of a regression function.
• Applications are diverse and relevant, and come from a variety of fields.
• The statistical software R, which is both widely used and freely available (thanks to the Free Software Foundation), is utilized. Using R rather than a commercial package enables students to work from the computer of their choice rather than in a computer lab.
• The generation of R software output is emphasized in addition to its interpretation.

About our author

Michael G. Akritas has been teaching Statistics at Penn State University since 1985. He is the author of approximately 100 research publications dealing with a wide range of statistical topics. He has supervised 18 Ph.D. and 12 M.Sc. students and is currently supervising 3 Ph.D. students. He is co-Founder of the International Society for Nonparametric Statistics, former Director of the National Statistical Consulting Center for Astronomy and co-Editor of the Journal of Nonparametric Statistics. He held a 3-year affiliation with the National Technical University of Athens, and visiting appointments at MIT, Texas A&M University, University of Pennsylvania, University of Göttingen, University of Cyprus, Australian National University, and UNICAMP. He has been an elected Fellow of the Institute of Mathematical Statistics and of the American Statistical Association since 2001.

NOTE: This edition features the same content as the traditional text in a convenient, three-hole-punched, loose-leaf version. Books a la Carte also offer a great value–this format costs significantly less than a new textbook. Before purchasing, check with your instructor or review your course syllabus to ensure that you select the correct ISBN. Several versions of Pearson’s MyLab & Mastering products exist for each title, including customized versions for individual schools, and registrations are not transferable. In addition, you may need a CourseID, provided by your instructor, to register for and use Pearson’s MyLab & Mastering products.

This text grew out of the author’s notes for a course that he has taught for many years to a diverse group of undergraduates. The early introduction to the major concepts of the course engages students immediately, which helps them see the big picture, and sets an appropriate tone. In subsequent chapters, these topics are revisited, developed, and formalized, but the early introduction helps students build a true understanding of the concepts. The text utilizes the statistical software R, which is both widely used and freely available (thanks to the Free Software Foundation). However, in contrast with other books for the intended audience, this book by Akritas emphasizes not only the interpretation of software output, but also the generation of this output. Applications are diverse and relevant, and come from a variety of fields.