Statistics and Data with R: An Applied Approach Through Examples

by Yosef Cohen (Univ. of Minnesota, USA ); Jeremiah Y. Cohen (Vanderbilt Univ. USA )

• ISBN13: 9780470721889
• ISBN10: 047072188X
• Format: eBook
• Copyright: 2008-11-01
• Publisher: Wiley

Summary

R, an Open Source software, has become the de facto statistical computing environment. It has an excellent collection of data manipulation and graphics capabilities. It is extensible and comes with a large number of packages that allow statistical analysis at all levels - from simple to advanced - and in numerous fields including Medicine, Genetics, Biology, Environmental Sciences, Geology, Social Sciences and much more. The software is maintained and developed by academicians and professionals and as such, is continuously evolving and up to date. Statistics and Data with R presents an accessible guide to data manipulations, statistical analysis and graphics using R.Assuming no previous knowledge of statistics or R, the book includes: A comprehensive introduction to the R language. An integrated approach to importing and preparing data for analysis, exploring and analyzing the data, and presenting results. Over 300 examples, including detailed explanations of the R scripts used throughout. Over 100 moderately large data sets from disciplines ranging from Biology, Ecology and Environmental Science to Medicine, Law, Military and Social Sciences. A parallel discussion of analyses with the normal density, proportions (binomial), counts (Poisson) and bootstrap methods. Two extensive indexes that include references to every R function (and its arguments and packages used in the book) and to every introduced concept.An accompanying Wiki website, http://turtle.gis.umn.edu includes all the scripts and data used in the book. The website also features a solutions manual, providing answers to all of the exercises presented in the book. Visitors are invited to download/upload data and scripts and share comments, suggestions and questions with other visitors. Students, researchers and practitioners will find this to be both a valuable learning resource in statistics and R and an excellent reference book.

Table of Contents

Preface

Data in statistics and R

Basic R

Preliminaries

Modes

Vectors

Arithmetic operators and special values

Objects

Programming

Packages

Graphics

Customizing the workspace

Projects

Assignments

Data in statistics and in R

Types of data

Objects that hold data

Data organization

Data import, export and connections

Data manipulation

Manipulating strings

Assignments

Presenting data

Tables and the flavors of apply ()

Bar plots

Histograms

Dot charts

Scatter plots

Lattice plots

Three-dimensional plots and contours

Assignments

Probability, densities and distributions

Probability and random variables

Set theory

Trials, events and experiments

Definitions and properties of probability

Conditional probability and independence

Algebra with probabilities

Random variables

Assignments

Discrete densities and distributions

Densities

Distribution

Properties

Expected values

Variance and standard deviation

The binomial

The Poisson

Estimating parameters

Some useful discrete densities

Assignments

Continuous distributions and densities

Distributions

Densities

Properties

Expected values

Variance and standard deviation

Areas under density curves

Inverse distributions and simulations

Some useful continuous densities

Assignments

The normal and sampling densities

The normal density

Applications of the normal

Data transformations

Random samples and sampling densities

A detour: using R efficiently

The sampling density of the mean

The sampling density of proportion

The sampling density of intensity

The sampling density of variance

Bootstrap: arbitrary parameters of arbitrary densities

Assignments

Statistics

Exploratory data analysis

Graphical methods

Numerical summaries

Visual summaries

Assignments

Point and interval estimation

Point estimation

Interval estimation

Point and interval estimation for arbitrary densities

Assignments

Single sample hypotheses testing

Null and alternative hypotheses

Large sample hypothesis testing

Small sample hypotheses testing

Arbitrary parameters of arbitrary densities

p-values

Assignments

Power and sample size for single samples

Large sample

Small samples

Power and sample size for arbitrary densities

Assignments

Two samples

Large samples

Small samples

Unknown densities

Assignments

Power and sample size for two samples

Two means from normal populations

Two proportions

Two rates

Assignments

Simple linear regression

Simple linear models

Estimating regression coefficients

The model goodness of fit

Hypothesis testing and confidence intervals

Model assumptions

Model diagnostics

Power and sample size for the correlation coefficient

Assignments

Analysis of variance

One-way, fixed-effects ANOVA

Non-parametric one-way ANOVA

One-way, random-effects ANOVA

Two-way ANOVA

Two-way linear mixed effects models

Assignments

Simple logistic regression

Simple binomial logistic regression

Fitting and selecting models

Assessing goodness of fit

Diagnostics

Assignments

Application: the shape of wars to come

A statistical profile of the war in Iraq

A statistical profile of the second Intifada

References

R Index

General Index

Table of Contents provided by Publisher. All Rights Reserved.