ST903 Statistical Methods
Please note that all lectures for Statistics modules taught in the 2022-23 academic year will be delivered on campus, and that the information below relates only to the hybrid teaching methods utilised in 2021-22 as a response to Coronavirus. We will update the Additional Information (linked on the right side of this page) prior to the start of the 2022/23 academic year.
Throughout the 2021-22 academic year, we will be adapting the way we teach and assess your modules in line with government guidance on social distancing and other protective measures in response to Coronavirus. Teaching will vary between online and on-campus delivery through the year, and you should read the additional information linked on the right hand side of this page for details of how this will work for this module. The contact hours shown in the module information below are superseded by the additional information.You can find out more about the University’s overall response to Coronavirus at: https://warwick.ac.uk/coronavirus.
All dates for assessments for Statistics modules, including coursework and examinations, can be found in the Statistics Assessment Handbook at http://go.warwick.ac.uk/STassessmenthandbook
ST903-15 Statistical Methods
Introductory description
This module runs in term 1 and is core for students on an MSc in Statistics course. It is not available for undergraduate students.
Module aims
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.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
The module content includes thorough grounding in classical methods of statistical inference with an introduction to more recent developments in statistical methodology. The following items are going to be covered: data, probability, random variables, special univariate distributions, joint and conditional distributions, distributions of functions of random variables, methods of inference, inference using simulation, maximum likelihood estimation, Baysian inference, general linear model.
Learning outcomes
By the end of the module, students should be able to:
- Understand basic probability and random variables.
- Make sense of univariate distributions, joint and conditional distributions and functions of random variables.
- Understand the principles of inference in particular Baysian inference and Maximum Likelihood Estimation.
- Apply linear models in general situations.
- Understand principles of and be able to apply statistical testing using the Likelihood Ratio approach.
- Gain familiarity with basic topics in computational statistics such as importance sampling, rejection sampling etc
Indicative reading list
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.
View reading list on Talis Aspire
Subject specific skills
TBC
Transferable skills
TBC
Study time
| Type | Required |
|---|---|
| Lectures | 30 sessions of 1 hour (20%) |
| Private study | 104 hours (69%) |
| Assessment | 16 hours (11%) |
| Total | 150 hours |
Private study description
Weekly revision of lecture notes and materials, wider reading, practice exercises and preparing for examination.
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Students can register for this module without taking any assessment.
Assessment group D3
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Assignment 2 | 10% | 7 hours | Yes (extension) |
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Due in Term 1 Week 10. |
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| Assignment 1 | 10% | 7 hours | Yes (extension) |
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Due in Term 1 Week 7. |
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| On-campus Examination | 80% | 2 hours | No |
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The examination paper will contain four questions, of which the best marks of THREE questions will be used to calculate your grade. ~Platforms - Moodle
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Assessment group R1
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| In-person Examination - Resit | 100% | No | |
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The examination paper will contain four questions, of which the best marks of THREE questions will be used to calculate your grade. ~Platforms - Moodle |
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Feedback on assessment
Marked assignments will be available for viewing at the support office within 20 working days of the submission deadline.
Solutions and cohort level feedback will be provided for the examination.
Courses
This module is Core for:
- Year 1 of TSTA-G4P1 Postgraduate Taught Statistics