Key facts
Details for course being taught in current academic year
Level 2 - 18 credits - spring and summer terms
E-learning links
Resources
Course description
Course outline
Elementary probability theory. Discrete and continuous univariate distributions. Probability mass, density and distribution functions. Mean and variance. Joint distributions. Sums of random variables, limits, the Central Limit Theorem. Sampling distributions.
Descriptive statistics. Some important distributions. Parameter estimation from data, including method of moments and maximum likelihood. Confidence intervals. Hypothesis testing. Linear regression.
Pre-requisite
Foundations in Analytical Skills, Mathematical Modelling
Learning outcomes
1 set up probability models for a range of random phenomena, both discrete and continuous;
2 apply the notions of conditional probability;
3 recognise where the use of certain standard probability distributions would be appropriate;
4 understand the principles of hypothesis testing, including power, and appropriately apply a range of statistical tests;
5 use a statistical package, both for numerical work and to help analyse data.
Assessments
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 20.00% | |
| Group Project | Summer Week 5 | 50.00% |
| Problem Sets | Summer Week 5 | 50.00% |
| Unseen Examination | Summer Term (1 hour 30 minutes) | 80.00% |
Resit mode of assessment
| Type | Timing | Weighting |
|---|---|---|
| Unseen Examination | Summer Vacation (1 hour 30 minutes) | 100.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
Teaching methods
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Spring+Summer Terms | LECTURE | 2 hours | 1111111111 |
| Spring+Summer Terms | WORKSHOP | 1 hour | 1111111111 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.