Probability & Statistics with R for Engineers and Scientists / Edition 1

by Michael Akritas

Overview

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.

Product Details

ISBN-13:	9780321852991
Publisher:	Pearson
Publication date:	01/10/2015
Edition description:	New Edition
Pages:	528
Product dimensions:	8.10(w) x 10.00(h) x 1.10(d)

About the Author

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.

Table of Contents

1. Basic Statistical Concepts
   • 1.1 Why Statistics?
   • 1.2 Populations and Samples
     • 1.2.1 Exercises
   • 1.3 Some Sampling Concepts
     • 1.3.1 Representative Samples
     • 1.3.2 Simple Random Sampling, and Stratied Sampling
     • 1.3.3 Sampling With and Without Replacement
     • 1.3.4 Non-representative Sampling
     • 1.3.5 Exercises
   • 1.4 Random Variables and Statistical Populations
     • 1.4.1 Exercises
   • 1.5 Basic Graphics for Data Visualization
     • 1.5.1 Histograms and Stem and Leaf Plots
     • 1.5.2 Scatterplots
     • 1.5.3 Pie Charts and Bar Graphs
     • 1.5.4 Exercises
   • 1.6 Proportions, Averages and Variances
     • 1.6.1 Population Proportion and Sample Proportion
     • 1.6.2 Population Average and Sample Average
     • 1.6.3 Population Variance and Sample Variance
     • 1.6.4 Exercises
   • 1.7 Medians, Percentiles and Box Plots
     • 1.7.1 Exercises
   • 1.8 Comparative Studies
     • 1.8.1 Basic Concepts and Comparative Graphics
     • 1.8.2 Lurking Variables and Simpson’s Paradox
     • 1.8.3 Causation: Experiments and Observational Studies
     • 1.8.4 Factorial Experiments: Main E