## Papers

**The available 200 and 300 level statistics papers are changing over the next 2 years**, following a review of the Statistics Programme. By 2020 the revised programme will be in place, and it will provide a clearer structure for students and updated content. However 2019 is an interim year.

Papers available each year (2019 – 2020) are listed first. Then the following section includes short descriptions of all papers, along with the years they are available. From there, clicking on the paper name will take you to a page with details of the paper.

**200-level and 300-level papers by year:**

In 2019: | STAT210, STAT260, STAT270, STAT311, STAT341, STAT342, STAT370, STAT372, STAT399 |

In 2020: | STAT210, STAT260, STAT270, STAT310, STAT311, STAT312, STAT370, STAT371, STAT372 |

See the flowchart of available papers for 2019, their prerequisites and semesters.

Click the paper name below for complete details.

Jump to 200 level, 300 level, 400 level

## 100 level

STAT110 Statistical Methods 18 points First Semester, Summer SchoolThis is a paper in statistical methods for students in the biological and social sciences covering descriptive statistics, probability distributions, estimation, hypothesis testing, regression, analysis of variance and experimental design. At the end of the course you should be able to make use of a wide variety of techniques in the design and analysis of your own research studies. The program R will be used for statistical analysis and data summary throughout the paper.

STAT115 Introduction to Biostatistics 18 points Second Semester

A paper for students in the health sciences covering an introduction to the research process and study design, measures for describing data, the binomial and normal distributions, estimation and inference for continuous data, estimation and inference for categorical data, regression procedures and statistical issues in study design. The statistical software R will be used throughout the paper to assist with data analysis.

## 200 level

STAT210 Applied Statistics 18 points First SemesterA core paper on using statistical models to address scientific questions. Regression models for continuous, binomial and count data, analysis of variance, cluster analysis, principal component analysis, research design. This paper is intended for students from all disciplines who are interested in learning more about the application of statistical methods.

STAT260 Visualisation and Modelling in R 18 points Second Semester

The software R is used to introduce computer skills needed for the statistical sciences. The course covers reproducible research, data wrangling, visualisation, exploratory data analysis, resampling and simulation.

STAT270 Probability and Inference 18 points First Semester

An introduction to the theory that underlies the statistical methods introduced in STAT110/115. Probability theory, random variables and distributions, expectation and variance, likelihoods, estimators and confidence intervals, hypothesis testing, and Bayesian inference.

## 300 level

STAT310 Statistical Modelling 18 points First SemesterAvailable from 2020

Statistical model building, motivated by real applications. Topics include regularisation, lasso, splines, non-linear regression, generalised linear models, model checking and introduction to mixed models.

STAT311 Design of Research Studies 18 points First Semester

Design of studies to address different types of research questions. Survey methods, experimental and observational studies, measurement, control of confounding and bias, evaluation of competing designs, determination of study size.

STAT312 Modelling High Dimensional Data 18 points Second Semester

Available from 2020

An introduction to the statistical learning techniques commonly used to analyse high-dimensional (or multivariate) data. Penalised regression, classification trees, clustering, dimension-reduction, bagging, stacking, boosting, random forests and ensemble learning.

STAT341 Regression and Modelling 2 18 points First Semester

Not available after 2019

An introduction to generalised linear models, non-linear regression models, and mixed effects models, with a mixture of background theory and practice in applying the methods to real datasets.

STAT342 Multivariate Methods 18 points Second Semester

Not available after 2019

This paper looks at procedures for the analysis and interpretation of data involving several response variables, and has wide application in research methodology of the biological, health and social sciences. Tests of significance for multivariate data, principal component analysis, exploratory and confirmatory factor analysis, methods of discrimination including the use of multinomial logistic regression models, canonical correlation analysis, cluster analysis, multivariate distance measures, multidimensional scaling, correspondence analysis and methods of ordination. SPSS 23 and its add-on AMOS23 are used throughout the paper although some use may be made of R.

Note that this paper is restricted against STAT 242 which has the same lectures but different assessment involving a project.

STAT370 Statistical Inference 18 points Second Semester

A continuation of the theoretical development begun in STAT 270, this paper will cover the theory of ordinary least squares, maximum likelihood estimation and inference, hypothesis testing, and Bayesian inference.

STAT371 Bayesian Data Analysis 18 points Second Semester

Available from 2020

Introduction to Bayesian methods with an emphasis on data analysis. Topics include prior choice, posterior assessment, hierarchical modelling and model fitting using R, JAGS and other freely available software.

STAT372 Stochastic Modelling 18 points First Semester

Introduction to statistical methods for processes observed over time and space. Poisson processes, renewal processes, Markov chains, hidden Markov models, geostatistics, spatial point processes, model fitting, forecasting and simulation.

STAT399 Special Topic – Statistical Computing 18 points Second Semester

The software R is used to introduce computer skills needed for the statistical sciences. The course covers reproducible research, visualisation, exploratory data analysis, optimization, resampling, simulation and R programming.