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Introductory Statistics with R

ISBN-10: 0387954759
ISBN-13: 9780387954752
Edition: 2004
Authors: Peter Dalgaard
List price: $59.95
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Description: R is an Open Source implementation of the well-known 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  More...

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Book details

List price: $59.95
Copyright year: 2004
Publisher: Springer
Publication date: 1/9/2004
Binding: Paperback
Pages: 267
Size: 6.25" wide x 9.25" long x 0.50" tall
Weight: 1.122
Language: English

R is an Open Source implementation of the well-known 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 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. Peter Dalgaard is associate professor at the Biostatistical Department at the University of Copenhagen and has extensive experience in teaching within the PhD curriculum at the Faculty of Health Sciences. He was chairman of the Danish Society for Theoretical Statistics from 1996 to 2000. Peter Dalgaard has been a key member of the R Core Team since August 1997 and is well known among R users for his activity on the R mailing lists.

Preface
Basics
First steps
An overgrown calculator
Assignments
Vectorized arithmetic
Standard procedures
Graphics
R language essentials
Expressions and objects
Functions and arguments
Vectors
Missing values
Functions that create vectors
Matrices and arrays
Factors
Lists
Data frames
Indexing
Conditional selection
Indexing of data frames
subset and transform
Grouped data and data frames
Sorting
Implicit loops
The graphics subsystem
Plot layout
Building a plot from pieces
Using par
Combining plots
R programming
Flow control
Classes and generic functions
Session management
The workspace
Getting help
Packages
Built-in data
attach and detach
Data entry
Reading from a text file
The data editor
Interfacing to other programs
Exercises
Probability and distributions
Random sampling
Probability calculations and combinatorics
Discrete distributions
Continuous distributions
The built-in distributions in R
Densities
Cumulative distribution functions
Quantiles
Random numbers
Exercises
Descriptive statistics and graphics
Summary statistics for a single group
Graphical display of distributions
Histograms
Empirical cumulative distribution
Q-Q plots
Boxplots
Summary statistics by groups
Graphics for grouped data
Histograms
Parallel boxplots
Stripcharts
Tables
Generating tables
Marginal tables and relative frequency
Graphical display of tables
Bar plots
Dotcharts
Pie charts
Exercises
One- and two-sample tests
One-sample t test
Wilcoxon signed-rank test
Two-sample t test
Comparison of variances
Two-sample Wilcoxon test
The paired t test
The matched-pairs Wilcoxon test
Exercises
Regression and correlation
Simple linear regression
Residuals and fitted values
Prediction and confidence bands
Correlation
Pearson correlation
Spearman's �
Kendall's �
Exercises
ANOVA and Kruskal-Wallis
One-way analysis of variance
Pairwise comparisons and multiple testing
Relaxing the variance assumption
Graphical presentation
Bartlett's test
Kruskal-Wallis test
Two-way analysis of variance
Graphics for repeated measurements
The Friedman test
The ANOVA table in regression analysis
Exercises
Tabular data
Single proportions
Two independent proportions
k proportions, test for trend
r � c tables
Exercises
Power and the computation of sample size
The principles of power calculations
The power of one-sample and paired t tests
Power of two-sample t test
Approximate methods
Power of comparisons of proportions
Two-sample problems
One-sample problems and paired tests
Comparison of proportions
Exercises
Multiple regression
Plotting multivariate data
Model specification and output
Model search
Exercises
Linear models
Polynomial regression
Regression through the origin
Design matrices and dummy variables
Linearity over groups
Interactions
Two-way ANOVA with replication
Analysis of covariance
Graphical description
Comparison of regression lines
Diagnostics
Exercises
Logistic regression
Generalized linear models
Logistic regression on tabular data
The analysis of deviance table
Connection to test for trend
Logistic regression using raw data
Prediction
Model checking
Exercises
Survival analysis
Essential concepts
Survival objects
Kaplan-Meier estimates
The log-rank test
The Cox proportional hazards model
Exercises
Obtaining and installing R
Data sets in the ISwR package
Compendium
Index

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