## 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. ```Contents
Preface
Chapter 1	Fundamentals
Everything Varies
Significance
Good and Bad Hypotheses
Null Hypotheses
p Values
Interpretation
Statistical Modelling
Maximum Likelihood
Experimental Design
The Principle of Parsimony (Occam's Razor)
Observation, Theory and Experiment
Controls
Replication: It's the n's that Justify the Means
How Many Replicates?
Power
Randomization
Strong Inference
Weak Inference
How Long to Go On?
Pseudoreplication
Initial Conditions
Orthogonal Designs and Non-orthogonal Observational Data

Chapter 2	Dataframes
Selecting Parts of a Dataframe: Subscripts
Sorting
Tidying Up
Chapter 3	Central Tendency
Chapter 4	Variance
Degrees of Freedom
Variance
A Worked Example
Variance and Sample Size
Using Variance
A Measure of Unreliability
Confidence Intervals
Bootstrap

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
Skew
Kurtosis

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
Caveats
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
Influence
Leverage
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
Aliasing
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
Odds
Overdispersion and Hypothesis Testing
Applications
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).