Using R for Introductory Statistics

Regular price €76.99
A01=John Verzani
analysis of variance methods
ANOVA Model
Author_John Verzani
base R programming
Basic Bootstrap Confidence Interval
BIVARIATE DATA
Category=PBT
Category=UFM
Ceo Compensation
Confidence Interval
confidence interval calculation
CONFIDENCE INTERVALS
Cumulative Distribution Function
Data Frame
Data Set
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
FALSE FALSE
FALSE FALSE FALSE
Highway Mileage
introductory statistics with R for students
knitr integration
knitr package
LINEAR REGRESSION
linear regression analysis
Min 1Q Median 3Q Max
Mosaic Plot
Multiple Linear Regression
MULTIVARIATE DATA
NA NA
NA NA NA
NA NA NA NA
NA NA NA NA NA
Parallel Coordinate Plot
Pivotal Quantity
R package
RStudio
RStudio workflow
Sampling Distribution
Scatter Plot
Simple Linear Regression Model
statistical computing
Type Ii Error
UNIVARIATE DATA
Verbal SAT Score
Xref Ref Type

Product details

  • ISBN 9781466590731
  • Weight: 1120g
  • Dimensions: 156 x 234mm
  • Publication Date: 26 Jun 2014
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
Delivery/Collection within 10-20 working days

Our Delivery Time Frames Explained
2-4 Working Days: Available in-stock

10-20 Working Days: On Backorder

Will Deliver When Available: On Pre-Order or Reprinting

We ship your order once all items have arrived at our warehouse and are processed. Need those 2-4 day shipping items sooner? Just place a separate order for them!

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.