Statistics at Square Two (2nd edition)

1. Models, Tests and Data
1.1 Basics
1.2 Models
1.3 Types of data
1.4 Significance tests
1.5 Confidence intervals
1.6 Statistical tests using models
1.7 Model fitting and analysis: confirmatory and exploratory analyses
1.8 Computer-intensive methods
1.9 Bayesian methods
1.10 Missing values
1.11 Reporting statistical results in the literature
1.12 Reading statistics in the literature

2. Multiple Linear Regression
2.1 The model
2.2 Uses of multiple regression
2.3 Two independent variables
2.4 Interpreting a computer output
2.5 Multiple regression in action
2.6 Assumptions underlying the models
2.7 Model sensitivity
2.8 Stepwise regression
2.9 Reporting the results of a multiple regression
2.10 Reading the results of a multiple regression

3. Logistic Regression
3.1 The model
3.2 Uses of logistic regression
3.3 Interpreting a computer output: grouped analysis
3.4 Logistic regression in action
3.5 Model checking
3.6 Interpreting computer output: ungrouped analysis
3.7 Case-control studies
3.8 Interpreting computer output: unmatched case-control study
3.9 Matched case-control studies
3.10 Interpreting computer output: matched case-control study
3.11 Conditional logistic regression in action
3.12 Reporting the results of logistic regression
3.13 Reading about logistic regression

4. Survival Analysis
4.1 Introduction
4.2 The model
4.3 Uses of Cox regression
4.4 Interpreting a computer output
4.5 Survival analysis in action
4.6 Interpretation of the model
4.7 Generalisations of the model
4.8 Model checking
4.9 Reporting the results of a survival analysis
4.10 Reading about the results of a survival analysis

5. Random Effects Models
5.1 Introduction
5.2 Models for random effects
5.3 Random vs fixed effects
5.4 Use of random effects models
5.5 Random effects models in action
5.6 Ordinary least squares at the group level
5.7 Computer analysis
5.8 Model checking
5.9 Reporting the results of random effects analysis
5.10 Reading about the effects of random effects analysis

6. Other Models
6.1 Possion regression
6.2 Ordinal regression
6.3 Time series regression
6.4 Reporting Poisson, ordinal or time series regression in the literature
6.5 Reading about the results of Poisson, ordinal or time series regression in the literature

Appendix 1. Exponentials and Logarithms
A1.1 Logarithms

Appendix 2. Maximum Likelihood and Significance Tests
A2.1 Binomial models and likelihood
A2.2 Poisson model
A2.3 Normal model
A2.4 Hypothesis testing: LR test
A2.5 Wald test
A2.6 Score test
A2.7 Which method to choose?
A2.8 Confidence intervals

Appendix 3: Bootstrapping and Variance Robust Standard Errors
A3.1 Computer analysis
A3.2 The bootstrap in action
A3.3 Robust or sandwich estimate SE
A3.4 Reporting the bootstrap and robust SEs in the literature

Appendix 4: Bayesian Methods
A4.1 Reporting Bayesian methods in the literature