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 todays 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.
Web Resources
A supplementary website offers solutions to odd exercises and templates for homework assignments while the data sets and R functions are available on CRAN.
Recenzijas
Praise for the First Edition:"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 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." 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
What Is R? Exploring Data. General Probability and Random Variables.
Univariate Probability Distributions. Multivariate Probability Distributions.
Sampling and Sampling Distributions. Point Estimation. Confidence Intervals.
Hypothesis Testing. Nonparametric Methods. Experimental Design. Regression.
Marķa 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.
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