1. Lecture 1 Statistical Modelling and Inference

2. Literature Main literature to be used:
Pawitan, Y.. (2001) In all likelihood : statistical modelling and inference using likelihood. Oxford : Clarendon.
Reference literature (Bayesian statistics): 
Gelman A., Carlin J.B., Stern H.S. and Rubin, D.B. (2004) Bayesian Data Analysis. Second Edition. Chapman & Hall. 

3. What is statistics? Planning – which data to collect (study design)
Describing – summarizing the data in a few quantiles (eg mean, variance)
Modelling – developing mathematical models with few parameters to represent patterns in data.
Inference – are the patterns we see real or not? Evaluation of the uncertainty in the parameters.
Model criticism – Is the model sensible for the data (eg residual analysis)

4. Statistical Modelling and Inference
On completion of this course, students will be able to: - analyze data with the R software using models for repeated measurement
- analyze data with the R software using Bayesian sampling methods - critically evaluate the output from these analyses
The students will be able to demonstrate knowledge of: - statistical inference for a broad range of statistical models - the assumptions behind the models - the philosophical differences between schools in statistics:
frequentist, likelihoodist and Bayesian

5. Topics to be covered Likelihood Linear mixed models Bayesian methods
Elements of likelihood inference
Using the entire likelihood function
Dealing with nuisance parameters
Evidence and the likelihood principle
Linear mixed models
Repeated measurements
Ranking for unbalanced data
Bayesian methods

6. Likelihood

7. Repeated Measurements

10. Spatial Modelling