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.
Statistical Analysis Overview
Introduction and orientation, motivation for formal statistical analysis.
Data Summary
Data summary, measures of location and dispersion and their meaning, skew
Discrete & Continuous Probability Models
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.
Statistical Significance
Hypothesis tests, statistical significance, p-values and their interpretation, confidence intervals
Contingency Tables
Tests applied to contingency tables.
Regression Models
Multiple linear and logistic regression models. Predictions from regression models.
Classification
Classification using regression type models.
The module will be delivered primarily through lectures, tutorials and laboratory work.
Peer-to-peer learning and mentoring in an on-line environment will be utilised to support students in developing their background, knowledge and communication in this topic.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Formal Examination | 50 |
| Other Assessment(s) | 50 |