This module will introduce students to the role of probability models and statistical inference in data analysis. Practical worked examples will give the student experience in applying probability and statistical models to real data using statistical software.
Data Summary
Summarise a data set, measures of location and dispersion and their meaning, skew. Use of graphical techniques to describe a dataset.
Probability Theory
Axioms of probability. Addition rule. Independence. Conditional probability. Multiplication rule. Bayes’ Theorem. Counting rules.
Discrete & Continuous Probability Distributions
Discrete and continuous distributions, means and standard deviations of probability distributions: Bernoulli, Binomial, Hypergeometric, Poisson, Multinomial and Normal probability distributions.
Hypothesis Testing
Null and alternative hypotheses. Statistical significance, p-values and their interpretation, confidence intervals. Types I and II errors. Z, chi-squared and t tests for single sample and two sample data. Chi-squared tests for contingency tables.
Linear & Logistic Regression
Linear and logistic regression models. Predictions and categorisation from regression models.
This module will be delivered through lectures, tutorials and practical statistical software exercises and assignments.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Other Assessment(s) | 100 |