This subject introduces the basic elements of statistical modelling, computation and data analysis. It is an entry point to further study of both mathematical and applied statistics, as well as broader data science.

Students will develop the ability to fit statistical models to data, estimate parameters of interest and test hypotheses. Both classical and Bayesian approaches will be covered. The importance of the underlying mathematical theory of statistics and the use of modern statistical software will be emphasised.

Concepts covered include: descriptive statistics, random sample, statistical inference, point estimation, interval estimation, properties of estimators, maximum likelihood, confidence intervals, hypothesis testing and Bayesian inference. Applications covered include: exploratory data analysis, inference for samples from univariate distributions, simple linear regression, correlation, goodness-of-fit tests and analysis of variance.