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The University of Southampton

STAT6117 Applied Statistical Modelling

Module Overview

This is a postgraduate advanced module in applied statistical modelling designed to equip students with highly sought after employability skills in data analysis. The module will cover a wide range of statistical models including a revision of introductory statistics, linear regression, logistic regression, multinomial logistic regression, log-linear models, models for rates (Poisson regression), and ordinal logistic regression. Some theory behind the methods will be covered, although the emphasis is on the practical application of these methods using statistical software. In this respect, students will be introduced to the statistical software of their choice Stata, SPSS or R.

Aims and Objectives

Module Aims

The aim is to provide you with a firm understanding of commonly used statistical modelling methods for analysing the relationship between variables. The emphasis will be on the practical application of these statistical techniques to quantitative data using statistical software and then interpreting and presenting the results.

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

  • Summarise data with an appropriate statistical model
  • Report writing
  • Use these models to describe the relationship between a response and a set of explanatory variables
  • Carry out and interpret statistical analyses such as hypothesis tests, linear regression and logistic regression
  • Check the model and interpret the results
  • Use of statistical software (SPSS/R/Stata) effectively
  • Gain an understanding of some of the basic theory behind statistical modelling.
  • Problem analysis and problem solving
  • Statistical computing
  • Data handling and manipulation


Normal distribution, sampling distributions and the central limit theorem, confidence intervals, hypothesis tests for means and proportions, chi-squared test of independence, two sample t-tests, correlation, simple linear regression, multiple linear regression, model selection and diagnostics, logistic regression, multinomial logistic regression, log-linear models, models for rates (Poisson regression), ordinal logistic regression.

Special Features

This module is one of the advanced quantitative modules at PG level that will equip our postgraduates with marketable skills in data analysis. The module will be taught by leading experts from Social Statistics and Demography.

Learning and Teaching

Teaching and learning methods

Teaching will be through a combination of lectures, tutorials and computer workshops. Learning activities will include learning in lectures, which will cover explanations of the statistical modelling techniques and their use, discussing problems during the tutorials, as well as by independent study. The computer workshops will provide hands-on experience of the analysis of data and the application of the techniques introduced in the lectures, enabling you to undertake the statistical computing element of the coursework assignment.

Independent Study160
Total study time200

Resources & Reading list

Field, A.. Discovering Statistics Using SPSS. 

Mehmetoglu, M. and Jakobsen, G. (2017). Applied Statistics using Stata. A Guide for the Social Sciences.. 

Agresti, A. (2013). Categorical Data Analysis. 

Fox, J. (2016). Applied Regression Analysis and Generalised Linear Models. 



MethodPercentage contribution
Coursework  (3500 words) 60%
Coursework  (3000 words) 40%


MethodPercentage contribution
Coursework  (3000 words) 40%
Coursework  (3500 words) 60%

Repeat Information

Repeat type: Internal & External

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