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The University of Southampton

STAT6095 Regression Modelling

Module Overview

This module will provide an overview of statistical methods for linear and logistic regression. Pre-requisite for: STAT6087, STAT6089, STAT6090, STAT6102 and STAT6103 One of the pre-requisites for STAT6091

Aims and Objectives

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

  • On successful completion of this module you will understand and be able to apply the different techniques involved in fitting regression models.
  • On successful completion of this module you will be able to apply regression methods to typical data sets arising in official statistics.
  • On successful completion of this module you will be able to use a statistical computing package to apply the different regression analysis techniques and understand and interpret the outputs.
  • On successful completion of this module you will be able to carry out an in-depth analysis of a dataset and undertake good statistical reporting.


Linear regression covering; • Basic (ordinary least squares) regression model • Residual analysis • Model building and selection for multiple regression model • Assessing model fit • Handling categorical variables, outliers, interactions, transformations • Spline regression, polynomial regression, Weighted Least Squares Introduction to logistic regression covering; • Basic model • Interpreting the parameters • Assessing model fit • Model building and selection

Learning and Teaching

Teaching and learning methods

Depending on feasibility, teaching may be delivered face to face intensively over a week, or online using a mixture of synchronous and asynchronous online methods, which may include lectures, discussion boards, workshop activities, exercises, and videos. A range of resources will also be provided for further self-directed study..

Independent Study72
Total study time100

Resources & Reading list

Field, A. (2013 / 2017). Discovering Statistics using IBM SPSS Statistics . 

Hosmer, W. H. , Lemeshow, S. and Sturdivant, T. (2013). Applied Logistic Regression (3rd edition). 

Agresti, A. (2013). An Introduction to Categorical Data Analysis. 

Laboratory space and equipment required. Computing Lab in SPSS.


Assessment Strategy

There will be opportunities to evaluate your progress through formative assessment, with summative assessment based on one online assignment.


MethodPercentage contribution
Coursework assignment(s) 100%


MethodPercentage contribution
Coursework assignment(s) 100%

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:

Approved Calculators

Candidates may use calculators in the examination room only as specified by the University and as permitted by the rubric of individual examination papers. The University approved model is Casio FX-570 This may be purchased from any source and no longer needs to carry the University logo.

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.


You will be expected to provide your own day-to-day stationery items, e.g. pens, pencils, notebooks, etc.


Where a module specifies core texts these should generally be available on the reserve list in the library. However due to demand, students may prefer to buy their own copies. These can be purchased from any source. Some modules suggest reading texts as optional background reading. The library may hold copies of such texts, or alternatively you may wish to purchase your own copies. Although not essential reading, you may benefit from the additional reading materials for the module.

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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