Dr Ric Crossman/Dr Panayiota Constantinou
Prerequisite(s): Either ST115 Introduction to Probability or ST111/2 Probability (taken concurrently).
Commitment: 3 lectures per week in weeks 6-10 of Term 2 and 1 lab each in weeks 7-10 of Term 2. 3 lectures per week in weeks 1-2 of Term 3, and 4 per week in weeks 3-4, along with 1 lab each in weeks 1-4. This module runs in Term 2 and 3 and is weighted at 12 CATS.
- 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.
- 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.
Leads to: ST221 Linear Statistical Modelling
• Thursday of Week 10: Lab report 1 (15%)
• Thursday of Week 3: Lab report 2 (15%)
Open-book examination (70%)
Feedback: Feedback to students will be given within 20 working days after the submission deadline. The results of the December examination will be available in week 10 of term 2.
Resources for Current ST104 Students (restricted access)