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

• Michael Akritas

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## Overview

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 you see the big picture and sets an appropriate tone. In subsequent chapters, these topics are revisited, developed and formalized, but the early introduction helps you 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.

ISBN-13: 9780137548552

Category: Probability & Statistics

## Overview

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

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