This module will introduce students to the role of probability models and statistical inference in data analysis. Laboratory work will give the student experience in applying probability and statistical models to real data. Peer-to-peer learning and mentoring in an on-line environment will be utilised to support students in developing their background and knowledge in this topic.
Preparatory background reading.
Introduction and orientation, motivation for formal statistical analysis.
Data summary, measures of location and dispersion and their meaning, skew.
Probability and probability models for data, calculating probabilities,
Discrete and continuous distributions, means and standard deviations of probability distributions: Bernoulli, Binomial, Hypergeometric, Poisson, Multinomial and Normal probability distributions. Multivariate Distributions.
Hypothesis tests, statistical significance, p-values and their interpretation, confidence intervals.
Tests applied to contingency tables and independence tests.
Linear and logistic regression models. Predictions and categorisation from regression models.
The module will be delivered primarily through lectures and tutorials which may be supplemented by online material.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Formal Examination | 70 |
| Other Assessment(s) | 30 |