Table of contents for Statistics : an introduction using R / M.J. Crawley.

Bibliographic record and links to related information available from the Library of Congress catalog.

Note: Contents data are machine generated based on pre-publication provided by the publisher. Contents may have variations from the printed book or be incomplete or contain other coding.

Chapter 1	Fundamentals
	Everything Varies
	Good and Bad Hypotheses
	Null Hypotheses
	p Values
	Statistical Modelling
	Maximum Likelihood
	Experimental Design
	The Principle of Parsimony (Occam's Razor)
	Observation, Theory and Experiment
	Replication: It's the n's that Justify the Means
	How Many Replicates?
	Strong Inference
	Weak Inference
	How Long to Go On?
	Initial Conditions
	Orthogonal Designs and Non-orthogonal Observational Data
Chapter 2	Dataframes
	Selecting Parts of a Dataframe: Subscripts
	Tidying Up
Chapter 3	Central Tendency
Chapter 4	Variance
	Degrees of Freedom
	A Worked Example
	Variance and Sample Size
	Using Variance
	A Measure of Unreliability
	Confidence Intervals
Chapter 5	Single Samples
	Data Summary in the One Sample Case
	The Normal Distribution
	Calculations using z of the Normal Distribution
	Plots for Testing Normality of Single Samples
	Inference in the One-sample Case
	Bootstrap in Hypothesis Testing with Single Samples
	Student's t-distribution
	Higher-order Moments of a Distribution
Chapter 6	Two Samples
	Comparing Two Variances
	Comparing Two Means
	Student's t-test
	Wilcoxon Rank Sum Test
	Tests on Paired Samples
	The Sign Test
	Binomial Tests to Compare Two Proportions
	Chi-square Contingency Tables
	Fisher's Exact Test
	Correlation and Covariance
	Data Dredging
	Partial Correlation
	Correlation and the Variance of Differences between Variables
	Scale-dependent Correlations
	Kolmogorov-Smirnov Test
Chapter 7	Statistical Modelling
	The Steps Involved in Model Simplification
	Order of Deletion
	Model Formulae in R
	Interactions between Explanatory Variables
	Multiple Error Terms
	The Intercept as Parameter 1
	Update in Model Simplification
	Examples of R Model Formulae
	Model Formulae for Regression
	GLMs: Generalized Linear Models
	The Error Structure
	The Linear Predictor
	Fitted Values
	The Link Function
	Canonical Link Functions
	Proportion Data and Binomial Errors
	Count Data and Poisson Errors
	GAMs: Generalized Additive Models
	Model Criticism
	Summary of Statistical Models in R
	Model Checking
	Non-constant Variance: Heteroscedasticity
	Non-normality of Errors
	Mis-specified Model
Chapter 8	Regression 
	Linear Regression
	Linear Regression in R
	Error Variance in Regression: SSY = SSR + SSF 
	Measuring the Degree of Fit, r2 
	Model Checking
	Polynomial Regression
	Non-linear Regression
	Testing for Humped Relationships
	Generalized Additive Models (GAMs)
Chapter 9	Analysis of Variance
	One-way Anova
	Shortcut Formulae
	Effect Sizes
	Plots for Interpreting One-way Anova
	Factorial Experiments
	Pseudoreplication: Nested Designs and Split Plots
	Split-plot Experiments
	Random Effects and Nested Designs
	Fixed or Random Effects?
	Removing the Pseudoreplication
	Analysis of Longitudinal Data
	Derived Variable Analysis
	Variance Components Analysis (VCA)
	What is the Difference between Split-plot and Hierarchical Samples?
Chapter 10	Analysis of Covariance
Chapter 11	Multiple Regression
	A Simple Example
	A More Complex Example
Chapter 12	Contrasts
	Contrast Coefficients
	An Example of Contrasts in R
	A Priori Contrasts
	Model Simplification by Step-wise Deletion
	Contrast Sums of Squares by Hand
	Comparison of the Three Kinds of Contrasts
	Contrasts and the Parameters of Ancova Models
	Multiple Comparisons
Chapter 13	Count Data
	A Regression with Poisson Errors
	Analysis of Deviance with Count Data
	The Danger of Contingency Tables
	Analysis of Covariance with Count Data
	Frequency Distributions
Chapter 14	Proportion Data
	Analyses of Data on One and Two Proportions
	Count Data on Proportions
	Overdispersion and Hypothesis Testing
	Logistic Regression with Binomial Errors
	Proportion Data with Categorical Explanatory Variables
	Analysis of Covariance with Binomial Data
Chapter 15	Death and Failure Data
	Survival Analysis with Censoring
Chapter 16	Binary Response Variable
	Incidence Functions
	Ancova with a Binary Response Variable
Appendix 1: Fundamentals of the R Language
	R as a Calculator
	Assigning Values to Variables
	Generating Repeats
	Generating Factor Levels
	Changing the Look of Graphics
	Reading Data from a File
	Vector Functions in R
	Subscripts: Obtaining Parts of Vectors
	Subscripts as Logical Variables
	Subscripts with Arrays
	Subscripts with Lists
	Writing Functions in R
	Sorting and Ordering
	Counting Elements within Arrays
	Tables of Summary Statistics
	Converting Continuous Variables into Categorical Variables using cut
	The split Function
	Trellis Plots
	The xyplot Function
	Three-dimensional (3-D) Plots
	Matrix Arithmetic
	Solving Systems of Linear Equations

Library of Congress Subject Headings for this publication:

Mathematical statistics -- Textbooks.
R (Computer program language).