Using R for Introductory Statistics

Description

The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version. See What’s New in the Second Edition: Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. Use of knitr package makes code easier to read and therefore easier to reason about. Additional information on computer-intensive approaches motivates the traditional approach. Updated examples and data make the information current and topical. The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text. The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing.

Get the book

Amazon.com

Search the book

eBay.com

Search the book

Other editions

• 

  Using R for Introductory Statistics

  • 2018-10-03
  • 518 pages
  • Ebook
  • CRC Press

Similar books

• 

  Introductory Statistics with R

  By Peter Dalgaard

  Cox, D. R. and Oakes, D. (1984), Analysis of Survival Data, Chapman & Hall, London. Everitt, B. S. (1994), A Handbook of Statistical Analyses Using S-PLUS, Chapman & Hall, London. Hájek, J., Šidák, Z., and Sen, P. K. (1999), ...

• 

  Introductory Statistics: A Conceptual Approach Using R

  By William B. Ware, John M. Ferron, Barbara Manning Miller

  Fisher also gave us an expression for the standard error (standard deviation) for his measure of skewness: SE = IL ... Mn+DZ¥K—Y)l. Kn_n,. (35m. g2. _. (n—l)(n—2)(n—3)s4. _. (n—2)(n—3). As you can see, at the heart of it is the sum of ...

• 

  Learning Statistics with R

  By Daniel Navarro

  After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book.

• 

  Solutions Manual for Using R for Introductory Statistics

  By John Verzani

  Solutions Manual for Using R for Introductory Statistics

• 

  Project-Based R Companion to Introductory Statistics

  By Chelsea Myers

  At the end of the book, there are several projects that require the use of multiple statistical techniques that could be used as a take-home final exam or final project for a class.

• 

  Introductory Statistics with R

  By Peter Dalgaard

  This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics.

• 

  Introductory Statistics: A Conceptual Approach Using R

  By William B. Ware, John M. Ferron, Barbara M. Miller

  Continuity of Examples – A master data set containing nearly all of the data used in the book’s examples is introduced at the beginning of the text. This ensures continuity in the examples used across the text.

• 

  Statistics: An Introduction using R

  By Michael J. Crawley

  Aitkin , M. , Anderson , D. , Francis , B. and Hinde , J. ( 1989 ) Statistical Modelling in GLIM . ... Caroll , R. J. and Ruppert , D. ( 1988 ) Transformation and Weighting in Regression . New York , Chapman and Hall .

• 

  Introduction to Data Science: Data Analysis and Prediction Algorithms with R

  By Rafael A. Irizarry

  This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful.

• 

  Introductory Statistics

  By Susan Dean, Barbara Illowsky, OpenStax

  Introductory Statistics includes innovative practical applications that make the text relevant and accessible, as well as collaborative exercises, technology integration problems, and statistics labs.