###### Lecturer(s): Dr Panayiota Constantinou

*Prerequisite(s)**:* Either ST115 Introduction to Probability or ST111/2 Probability (taken concurrently).

* Leads to:* ST221 Linear Statistical Modelling

* Commitment:* This module runs in Term 2 and 3 and is weighted at 12 CATS.

Term 2: 3 lectures each in weeks 6-10 and 1 lab each in weeks 7-10,

Term 3: 3 lectures each in weeks 1-2, 4 lectures each in weeks 3-4 and 1 lab each in weeks 1-4.

*Content:*

- Introduction to R
- Exploratory data analysis: methods of visualisation and summary statistics
- Sampling from standard discrete and continuous distributions (Bernoulli, Geometric, Poisson, Gaussian, Gamma)
- Generic methods for sampling from univariate distributions
- The use of R to illustrate probabilistic notions such as conditioning, convolutions and the law of large numbers
- Examples of modelling real data (but without formal statistical inference) and the use of visualisations to assess fit

** Aims**: To introduce students to the R software package, making use of it for exploratory data analysis and simple simulations. This should deepen and reinforce the understanding of probabilistic notions being learnt in ST115 and ST111/2.

** Objectives**:

- A familiarity with the R software package, making use of it for exploratory data analysis.
- An understanding of elementary simulation techniques applied to probability.
- The ability to propose appropriate probabilistic models for simple data sets.

** Assessment: **30% assessed work and 70% open-book examination.

*Deadlines**:*

Term 2: Thursday of Week 10: Lab report 1 **(15%)**

Term 3: Thursday of Week 3: Lab report 2 **(15%)**

* Feedback: *Feedback to students will be given within 20 working days after the submission deadline.