Amazon cover image
Image from Amazon.com
Image from Google Jackets

Introductory statistics with R / Peter Dalgaard.

By: Material type: TextTextSeries: Statistics and computing | Publication details: New York : Springer, c2008.Edition: 2nd edDescription: xvi, 363 p. : ill. ; 24 cmISBN:
  • 9780387790534
  • 0387790535
Subject(s): DDC classification:
  • 519.502 22 DAL
Contents:
Basics -- The R environment -- Probability and distributions -- Descriptive statistics and graphics -- One- and two-sample tests -- Regression and correlation -- Analysis of variance and the Kruskal-Wallis test -- 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 curve fitting.
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Collection Call number Status Date due Barcode
General Books General Books CUTN Central Library Sciences Non-fiction 519.502 DAL (Browse shelf(Opens below)) Available 28090

This book provides an elementary-level introduction to R, targeting both non-statistician 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 two-sample tests with continuous data, regression analysis, one-and two-way 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.

Basics --
The R environment --
Probability and distributions --
Descriptive statistics and graphics --
One- and two-sample tests --
Regression and correlation --
Analysis of variance and the Kruskal-Wallis test --
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 curve fitting.

Includes bibliographical references (p. [355]-356) and index.

There are no comments on this title.

to post a comment.

Powered by Koha