Regression and Simulation Methods
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Within the Probability & Statistics theme, there are two free-standing and independently-assessed modules on statistics. (You can take either one, neither, or both.) One aim is to introduce some key topics which lie at the heart of research in statistical methods and form a basis for more advanced and sophisticated ideas. Another is to develop good computational skills using R, the statistical computing package which is in widespread use for statistical work in academia and industry.
Regression & Simulation Methods (first semester - this module)
- Introduction to R
- Linear models
- Likelihood methods and optimisation
- Generalised linear models
- Simulation and bootstrapping
- Case study
This module is assessed through a single assignment, based on the 5 lectures on GLMs and simulation. The deadline for the assignment will be in early January 2020. The assignment will be available by mid-December.
Basic concepts in
- probability (elementary probability distributions)
- statistics (ideas of estimation, confidence intervals, hypothesis testing)
- linear algebra
The level required in these areas would usually be provided in a first undergraduate course.
Important: The first five sessions of Regression and Simulation methods cover material which is a standard part of many undergraduate curricula, although it is recognised that the material may be new to some. A flexible form of delivery allows participants to study different parts of this material at a speed and depth which is appropriate to their previous experience. So, the first five sessions of this module will be delivered through audio and PDF files supplied on the SMSTC website. There will be no videoconference sessions until session 6 (12 November 2019), when the style of delivery will revert to the usual videoconferencing lecture. However, during the first five weeks there will be local tutorials to support the learning of the material. This is likely to focus largely on the exercises, but there should also be an opportunity to discuss the lecture material if that would be useful.