8596 modules
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MATH2056 2027-28
Statistical Modelling I
Statistical Modelling I offers a comprehensive study of maximum likelihood estimation and multiple linear regression, covering both estimation and inferential procedures. The theoretical framework is formulated using vector and matrix methods. Techniques for model diagnostics, assessment of adequacy, and model selection are also covered. -
MATH2056 2026-27
Statistical Modelling I
Statistical Modelling I offers a comprehensive study of maximum likelihood estimation and multiple linear regression, covering both estimation and inferential procedures. The theoretical framework is formulated using vector and matrix methods. Techniques for model diagnostics, assessment of adequacy, and model selection are also covered. -
MATH2010 2026-27
Statistical Modelling I
Simple linear regression is developed for one explanatory variable using the principle of least squares. The extension to two explanatory variables raises the issue of whether both variables are needed for a well-fitting model, or whether one is sufficient and, if so, which one.
These ideas are generalised to many explanatory variables (multiple regression), for which the necessary theory of linear models is developed in terms of vectors and matrices. Checking model adequacy is introduced, e.g. by examining plots of the residuals. Widening the class of models that can be considered by the use of dummy variables for qualitative explanatory variables to assess treatment effects.
The methods are implemented using a suitable software and students gain experience and advice through weekly worksheets.
One of the pre-requisites for MATH3012, MATH3013, MATH3014, MATH6021, MATH6025, MATH6027 and MATH6135 -
MATH2010 2027-28
Statistical Modelling I
Simple linear regression is developed for one explanatory variable using the principle of least squares. The extension to two explanatory variables raises the issue of whether both variables are needed for a well-fitting model, or whether one is sufficient and, if so, which one.
These ideas are generalised to many explanatory variables (multiple regression), for which the necessary theory of linear models is developed in terms of vectors and matrices. Checking model adequacy is introduced, e.g. by examining plots of the residuals. Widening the class of models that can be considered by the use of dummy variables for qualitative explanatory variables to assess treatment effects.
The methods are implemented using a suitable software and students gain experience and advice through weekly worksheets.
One of the pre-requisites for MATH3012, MATH3013, MATH3014, MATH6021, MATH6025, MATH6027 and MATH6135 -
MATH2010 2028-29
Statistical Modelling I
Simple linear regression is developed for one explanatory variable using the principle of least squares. The extension to two explanatory variables raises the issue of whether both variables are needed for a well-fitting model, or whether one is sufficient and, if so, which one.
These ideas are generalised to many explanatory variables (multiple regression), for which the necessary theory of linear models is developed in terms of vectors and matrices. Checking model adequacy is introduced, e.g. by examining plots of the residuals. Widening the class of models that can be considered by the use of dummy variables for qualitative explanatory variables to assess treatment effects.
The methods are implemented using a suitable software and students gain experience and advice through weekly worksheets.
One of the pre-requisites for MATH3012, MATH3013, MATH3014, MATH6021, MATH6025, MATH6027 and MATH6135 -
MATH3091 2027-28
Statistical Modelling II
The module Statistical Modelling II covers in detail the theory of linear regression models, where explanatory variables are used to explain the variation in a response variable, which is assumed to be normally distributed. However, in many practical situations the data are not appropriate for such analysis. For example, the response variable may be binary, and interest may be focused on assessing the dependence of the probability of 'success' on potential explanatory variables. Such techniques are important in many disciplines such as finance, market research and medicine.
Alternatively, a variety of biological and social science data are in the form of cross-classified tables of counts, called contingency tables. The structure of such tables can be examined to determine the pattern of interdependence of the cross-classifying variables. -
MATH3091 2028-29
Statistical Modelling II
The module Statistical Modelling II covers in detail the theory of linear regression models, where explanatory variables are used to explain the variation in a response variable, which is assumed to be normally distributed. However, in many practical situations the data are not appropriate for such analysis. For example, the response variable may be binary, and interest may be focused on assessing the dependence of the probability of 'success' on potential explanatory variables. Such techniques are important in many disciplines such as finance, market research and medicine.
Alternatively, a variety of biological and social science data are in the form of cross-classified tables of counts, called contingency tables. The structure of such tables can be examined to determine the pattern of interdependence of the cross-classifying variables. -
STAT6103 2025-26
Statistical Programming
This module aims to give students a grounding in the use of statistical software for data manipulation, analysis and simulation. It uses the R software as a basis, but also introduces students to the Python programming language, as both tools have wide functionality and close links with data science. -
STAT6146 2026-27
Statistical Programming in Python
This module aims to give students a grounding in the use of statistical software for data manipulation and analysis in Python. -
PSYC6055 2025-26
Statistical Programming in R
The Statistical Programming in R Module is focused on extending existing skills in analyzing data from quantitative research. The focus of this course will not be on extensively expanding the mathematical knowledge of the techniques employed but will be on acquiring practical skills such as scripting, flexible matrix manipulation and advanced visualization. All these skills are particularly useful when confronted with especially large datasets, and when confronted with a multitude of repetitive statistical
procedures needing implementation. This module will also cover an introduction into Linear Mixed Models. Analyses will be implemented using the interactive programming environment known as R. R is a free, open source programming language for statistical analysis.