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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

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.

Learning and Teaching

Teaching and learning methods

Teaching will be delivered by a mixture of synchronous and asynchronous online methods, which may include lectures, quizzes, discussion boards, workshop activities, exercises, and videos. A range of resources will also be provided for further self-directed study. Face-to-face teaching opportunities will be explored depending on circumstances and feasbility.

Independent Study160
Total study time200

Resources & Reading list

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

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

Field, A.. Discovering Statistics Using SPSS. 

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



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


Costs associated with this module

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.

In addition to this, students registered for this module typically also have to pay for:

Books and Stationery equipment

Recommended texts for this module may be available in limited supply in the University Library and students may wish to purchase reading texts as appropriate.

Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at

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