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Probability & Statistics with R for Engineers and Scientists, 1st edition

Published by Pearson Prentice Hall (March 21, 2018) © 2019

  • Michael Akritas The Pennsylvania State University

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For courses in Probability & Statistics for engineering and science students.

A modern classic

Probability & Statistics with R for Engineers and Scientists, 1st Edition 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. It utilizes the statistical software R, which is both widely used and freely available (thanks to the Free Software Foundation). However, unlike other books on the subject, it emphasizes not only the interpretation of software output but the generation of this output. Applications are diverse and relevant, and come from a variety of fields.

This title is part of the Pearson Modern Classics series. Pearson Modern Classics are acclaimed titles at a value price.

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.
  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

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.

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