Cohesively Incorporates Statistical Theory with R Implementation
Since the publication of the popular first edition of this comprehensive textbook, the contributed R packages on CRAN have increased from around 1,000 to over 6,000. Designed for an intermediate undergraduate course, Probability and Statistics with R, Second Edition explores how some of these new packages make analysis easier and more intuitive as well as create more visually pleasing graphs.
New to the Second Edition
- Improvements to existing examples, problems, concepts, data, and functions
- New examples and exercises that use the most modern functions
- Coverage probability of a confidence interval and model validation
- Highlighted R code for calculations and graph creation
Gets Students Up to Date on Practical Statistical Topics
Keeping pace with today’s statistical landscape, this textbook expands your students’ knowledge of the practice of statistics. It effectively links statistical concepts with R procedures, empowering students to solve a vast array of real statistical problems with R.
A supplementary website offers solutions to odd exercises and templates for homework assignments while the data sets and R functions are available on CRAN.
Table of Contents
What Is R?
Introduction to R
Downloading and Installing R
Mode and Class of an Object
R Data Structures
Reading and Saving Data in R
Working with Data
Using Logical Operators with Data Frames
Coordinate Systems and Traditional Graphic’s States
What Is Statistics?
Displaying Qualitative Data
Displaying Quantitative Data
Summary Measures of Location
Summary Measures of Spread
Complex Plot Arrangements
General Probability and Random Variables
Moment Generating Functions
Univariate Probability Distributions
Discrete Univariate Distributions
Continuous Univariate Distributions
Multivariate Probability Distributions
Joint Distribution of Two Random Variables
Independent Random Variables
Several Random Variables
Expected Values, Covariance, and Correlation
Bivariate Normal Distribution
Sampling and Sampling Distributions
Sampling Distribution of the Sample Mean
Sampling Distribution for a Statistic from an Infinite Population
Sampling Distributions Associated with the Normal Distribution
Properties of Point Estimators
Point Estimation Techniques
Confidence Intervals for Population Means
Confidence Intervals for Population Variances
Confidence Intervals Based on Large Samples
Type I and Type II Errors
Uniformly Most Powerful Test
ρ-Value or Critical Level
Tests of Significance
Hypothesis Tests for Population Means
Hypothesis Tests for Population Variances
Hypothesis Tests for Population Proportions
Wilcoxon Signed-Rank Test
The Wilcoxon Rank-Sum or the Mann-Whitney U-Test
The Kruskal-Wallis Test
Friedman Test for Randomized Block Designs
Categorical Data Analysis
Fixed Effects Model
Analysis of Variance (ANOVA) for the One-Way Fixed Effects Model
Power and the Non-Central F Distribution
Multiple Comparisons of Means
Other Comparisons among the Means
Summary of Comparisons of Means
Random Effects Model (Variance Components Model)
Randomized Complete Block Design
Two-Factor Factorial Design
Simple Linear Regression
Multiple Linear Regression
Ordinary Least Squares
Properties of the Fitted Regression Line
Using Matrix Notation with Ordinary Least Squares
The Method of Maximum Likelihood
The Sampling Distribution of β
ANOVA Approach to Regression
General Linear Hypothesis
Interpreting a Logarithmically Transformed Model
Estimation of the Mean Response for New Values Xh
Prediction and Sampling Distribution of New Observations Yh(new)
Simultaneous Confidence Intervals
Appendix A: R Commands
Appendix B: Quadratic Forms and Random Vectors and Matrices
Problems appear at the end of each chapter.
María Dolores Ugarte is a professor of statistics in the Department of Statistics and Operations Research at the Public University of Navarre (UPNA). She is an associate editor of Statistical Modelling, TEST, and Computational Statistics and Data Analysis and an editorial board member of Spatial and Spatio-temporal Epidemiology. She received a rating of "Excellent Teacher" from UPNA in 2008 and the INNOLEC Lectureship Award from Masaryk University in 2007. She earned a PhD in statistics from UPNA and completed her postdoctoral training in the Department of Mathematics and Statistics at Simon Fraser University.
Ana F. Militino is a professor of statistics at the Public University of Navarre. She is co-editor in chief of TEST, official journal of the Spanish Society of Statistics and Operations Research. She received the John Griffiths teaching award in 2011 and was a visiting researcher at Oxford University and Simon Fraser University. She earned a PhD in statistics from the University of Extremadura.
Alan T. Arnholt is a professor in the Department of Mathematical Sciences at Appalachian State University, where he has taught undergraduate and graduate statistics since 1993. He earned a PhD in applied statistics from the University of Northern Colorado.
Praise for the First Edition:"The book is comprehensive and well written. The notation is clear and the mathematical derivations behind nontrivial equations and computational implementations are carefully explained. Rather than presenting a collection of R scripts together with a summary of relevant theoretical results, this book offers a well-balanced mix of theory, examples and R code."
"This book covers a wide range of topics in both theoretical and applied statistics … Detailed executable codes and codes to generate the figures in each chapter are available online … nicely blend[s] mathematical statistics, statistical inference, statistical methods, and computational statistics using S language ... . Students or self-learners can learn some basic techniques for using R in statistical analysis on their way to learning about various topics in probability and statistics. This book also could serve as a wonderful stand-alone textbook in probability and statistics if the computational statistics portions are skipped."
—Technometrics, May 2009
—The American Statistician, February 2009
"… an impressive book … this is a good reference book with comprehensive coverage of the details of statistical analysis and application that the social researcher may need in their work. I would recommend it as a useful addition to the bookshelf."
—Significance, December 2008
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