Introductory statistics with R
 Author/Creator
 Dalgaard, Peter.
 Language
 English.
 Edition
 2nd ed.
 Imprint
 New York : Springer, c2008.
 Physical description
 xvi, 363 p. : ill. ; 24 cm.
 Series
 Statistics and computing.
Access
Available online
 dx.doi.org SpringerLink
Course reserve
 Course
 STATS14101  Biostatistics
 Instructor(s)
 Mukherjee, Rajarshi
 Course
 STATS30501  Introduction to Statistical Modeling
 Instructor(s)
 Owen, Art B

Request at circulation desk

Unknown
QA276.45 .R3 D33 2008
On Reserve 1day loan

Unknown
QA276.45 .R3 D33 2008
More options
Contents/Summary
 Bibliography
 Includes bibliographical references (p. [355]356) and index.
 Contents

 Basics.  The R environment.  Probability and statistics.  Descriptive statistics and graphics.  One and two sample tests.  Regression and correlation.  ANOVA and KruskalWallis.  Tabular data.  Power and the computation of sample size.  Advanced data handling.  Multiple regression.  Linear models.  Logistic regression.  Survival analysis.  Rates and Poisson regression.  Nonlinear curvefitting.  Obtaining and installing R and the ISwR package.  Data sets in the ISwR package.  Compendium.  Answers to exercises.  Index.
 (source: Nielsen Book Data)
 Publisher's Summary
 R is an Open Source implementation of the wellknown S language. It works on multiple computing platforms and can be freely downloaded. R is thus ideally suited for teaching at many levels as well as for practical data analysis and methodological development. This book provides an elementarylevel introduction to R, targeting both nonstatistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint.Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one and twosample tests with continuous data, regression analysis, one and twoway analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.
(source: Nielsen Book Data)
Subjects
Bibliographic information
 Publication date
 2008
 Responsibility
 Peter Dalgaard.
 Series
 Statistics and computing
 ISBN
 9780387790534
 0387790535
 9780387790541
 0387790543