ST2132 Mathematical Statistics

Module Description
This module introduces students to the theoretical underpinnings of statistical methodology and concentrates on inferential procedures within the framework of parametric models. Topics include: random sample and statistics, method of moments, maximum likelihood estimate, Fisher information, sufficiency and completeness, consistency and unbiasedness, sampling distributions, x2-, t- and F- distributions, confidence intervals, exact and asymptotic pivotal method, concepts of hypothesis testing, likelihood ratio test, Neyman-Pearson lemma. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisite.

Topics
- Normal Distribution and some related distribution
- Survey Sampling
- Parameter Estimation
- The Fisher Information
- Large Sample Theory for MLE
- Efficiency
- Sufficiency
- Hypothesis Testing
- Generalized Likelihood Ratio Test
- Comparing Two Samples

Workload
Two two-hours lectures 
This module was taught by Dr Lim Chinghway. He was clear and concise in his delivery. On top of that, he would actually ask us if he was going too fast or we needed him to repeat. At least for me, I encountered very few lecturers who actually does that so I really appreciated that. Recommended to take this module if he is teaching :D

Weekly one-hour tutorials
Difficulty was progressive (as usual). Starting off with simpler questions to ease us towards the harder and more insightful questions.

Textbook is recommended. 
The textbook is nice to have if you have trouble following the lectures or you don't want to watch the webcast.

Lectures were webcasted.

Assessment
50% Final Exam
20% Midterm Exam
5% Tutorial Attendance
5% Tutorial Presentation (Need to present answers for 1-2 questions)
20% Tutorial Submission
5% (Bonus for forum participation)

Extra notes to readers: If you want to know more about the modules I have taken, you may refer to this list or the labels to the right! :D