MAS363 Linear Models

Note: This is an old module occurrence.

You may wish to visit the module list for information on current teaching.

Semester 1, 2014/15 10 Credits
Lecturer: Dr Kostas Triantafyllopoulos uses MOLE Timetable Reading List
Aims Outcomes Assessment

The course will develop the general theory of linear models, an important class of statistical models. It will also discuss model building and model checking in the context of regression. Multiple regression will be developed in some detail. Illustration using the R software will be given throughout.

Prerequisites: MAS205 (Statistics Core); MAS273 (Statistical Modelling) recommended
Not with: MAS463 (Linear Models)

The following modules have this module as a prerequisite:
MAS370Sampling Theory and Design of Experiments
MAS473Extended Linear Models

Outline syllabus

  • Simple Linear Regression: Brief introductory examples on regression and the analysis of variance.
  • The General Linear Model: The general linear model; reduced models; replicates and lack of fit; weighted and generalized least-squares.
  • Diagnostics and Model Revision: Examination of residuals; types of residuals; influential observations; transformations.
  • More Linear Models and Model Building: Use of the flexibility of the general linear model; strategy for model-building and variable selection.

Office hours

This course uses MOLE2 - you are welcome to post your questions there. Otherwise come and see me between 2 and 3 on Mondays in I10, or send me an email if you book at appointment.


  • To review and extend the students knowledge of the standard linear model.
  • To develop enough of the theory to allow a proper understanding of what these methods can achieve.
  • To show how these methods are applied to data, and what kinds of conclusion are possible.

Learning outcomes

  • Obtain a technical understanding and appreciation of regression and ANOVA methods.
  • Apply a variety of regression methods using the programming language R to real-data.
  • Perform statistical analyses through several data sets and make critical interpretations of the results.

20 lectures, no tutorials


One formal 2 hour written examination. Format: 3 questions from 4.

Reading list

Type Author(s) Title Library Blackwells Amazon
C Atkinson Plots, Transformations and Regression 519.51 (A) Blackwells Amazon
C Cook and Weisberg Residuals and Influence in Regression 519.51 (C) Blackwells Amazon
C Draper and Smith Applied Regression Analysis 519.536 (D) Blackwells Amazon
C Montgomery, Peck and Vining Introduction to Linear Regression Analysis 519.51 (M) Blackwells Amazon
C Seber and Lee Linear Regression Analysis 519.51 (S) Blackwells Amazon

(A = essential, B = recommended, C = background.)

Most books on reading lists should also be available from the Blackwells shop at Jessop West.


Thu 09:00 - 09:50 lecture   Hicks Lecture Theatre 2