Lecturer(s): Dr Rito Dutta, Professor Barbel Finkenstadt-Rand
Commitment: 3 hours per week for 10 weeks. This module runs in Term 1.
Summary: The module content will include a thorough grounding in classical and Bayesian methods of statistical inference with an introduction to selected more recent developments in statistical methodology. Since MSc students have different background knowledge in statistics we start afresh. At the end of the course you will have a solid background in basic statistics and knowledge at an advanced level in some areas.
- Random Variables
- Univariate distributions
- Multiple Random Variables
- Properties of random samples
- Statistics, Sufficiency and Likelihood
- Point Estimation
- Hypothesis Testing and Interval Estimation
- Elements of Bayesian Inference
- Generalised linear model
Casella, G. and Berger, R. L., Statistical Inference, 2nd Ed, Duxbury.
Wasserman L.,All of Statistics: A Concise Course in Statistical Inference, Springer
An Introduction to Probability and Statistical Inference (second edition), by G.G. Roussas
Lecture notes will cover everything that is done in the course.
Assessment: 90% Exam in Week 1 of Term 2, 10% Course Work.
Deadline: Homework Test 1: Wednesday week 7 and Homework test 2: Wednesday week 10.
Feedback: Feedback on both homework tests will be returned after 2 weeks, following submission. The results of the January examination will be available in week 10 of term 2.
Examination Period: January