1
The overall approach
1.1
The objectives
1.2
The methodology
2 Planning,
analysing and interpreting data
2.1
Statistical programs
2.2
Management of experimental data
2.3
Types of data
2.4
Types of analysis
2.5
Types of variables
2.6
Numbers of treatments
2.7
Numbers of replicates
2.8
Blocks
2.9
Covariates
3
Analysis of continuous data
3.1
Experiments with two treatments
3.2
Relationships between variables
3.3
Experiments with more than two treatments
3.4
Experiments with blocks
3.5
Latin square design
3.6
Experiments with interactions
3.7
Introducing covariates
3.8
Better experiments (more levels of the treatments)
3.9
Dealing with unbalanced designs
3.10
Some Restrictions on Models in GLM
4
Analysing experiments with discontinuous variables
4.1
Chi-squared analysis
4.2
Numbers required for chi-squared analysis (eg: animal reproductive performance)
4.3
Limitations of chi-squared analysis
4.5
Exact probabilities
4.6
Other non-parametric tests
4.7
Transforming non-normal data for analysis
4.7.1
Square root transformation
4.7.2
The logarithmic transformation
4.7.3
The angular transformation
5
Simulating experimental data