Introductory Statistics with R / Edition 2

Introductory Statistics with R / Edition 2

by Peter Dalgaard
     
 

ISBN-10: 0387790535

ISBN-13: 9780387790534

Pub. Date: 04/24/2009

Publisher: Springer New York

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…  See more details below

Overview

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.

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Product Details

ISBN-13:
9780387790534
Publisher:
Springer New York
Publication date:
04/24/2009
Series:
Statistics and Computing Series
Edition description:
2nd ed. 2008
Pages:
364
Sales rank:
1,129,849
Product dimensions:
6.10(w) x 9.20(h) x 0.80(d)

Table of Contents

Basics. - The R environment. - Probability and statistics. - Descriptive statistics and graphics. - One and two sample tests. - Regression and correlation. - ANOVA and Kruskal-Wallis. - 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. - Obtaining and installing R and the ISwR package. - Data sets in the ISwR package. - Compendium. - Answers to exercises. - Index.

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