MAS223 Statistical Inference and Modelling

Note: This is an old module occurrence.

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

Both semesters, 2019/20 20 Credits
Lecturer: Dr Alison Poulston uses MOLE Timetable Reading List
Aims Outcomes Assessment Full Syllabus

This unit develops methods for analysing data, and provides a foundation for further study of probability and statistics at Level 3. It introduces some standard distributions beyond those met in MAS113, and proceeds with study of continuous multivariate distributions, with particular emphasis on the multivariate normal distribution. Transformations of univariate and multivariate continuous distributions are studied, with the derivation of sampling distributions of important summary statistics as applications. The concepts of likelihood and maximum likelihood estimation are developed. Data analysis is studied within the framework of linear models. There will be substantial use of the software package R.

Prerequisites: MAS113 (Introduction to Probability and Statistics)
Corequisites: MAS211 (Advanced Calculus and Linear Algebra)

The following modules have this module as a prerequisite:

MAS352Stochastic Processes and Finance
MAS360Practical and Applied Statistics
MAS361Medical Statistics
MAS362Financial Mathematics
MAS364Bayesian Statistics
MAS367Linear and Generalised Linear Models
MAS369Machine Learning
MAS370Sampling Theory and Design of Experiments
MAS371Applied Probability
MAS372Time Series
MAS452Stochastic Processes and Finance
MAS461Medical Statistics
MAS462Financial Mathematics
MAS464Bayesian Statistics
MAS467 Linear and Generalised Linear Models
MAS468Statistical Computing in R
MAS469Machine Learning
MAS61007Machine learning

Outline syllabus

  • Univariate distribution theory
  • Multivariate distribution theory
  • Likelihood
  • Likelihood case studies
  • Linear models

Office hours

Semester 2:

Office hours are 1-2pm on Wednesday in G27C, Hicks, for students to ask the lecturer questions about the module. Please try to email in advance if you would like to meet during this time. You can also just pop by, but I will give preference to those who have a pre-arranged slot.

If you cannot make Wednesday 1-2pm, and would like to meet, please email me to arrange another time.

Please do not come to my office outside my office hours without pre-arrangement as I will be busy with other work and will have to turn you away.


  • extend students' familiarity with standard probability distributions;
  • give practice in handling discrete and continuous distributions, especially continuous multivariate ones;
  • instil an understanding of the rationale and techniques of likelihood exploration and maximisation;
  • consider linear regression models in detail;
  • extend the comparison of means from two to several groups through ANOVA models;
  • give students experience in the use of R for fitting linear models;

Learning outcomes

  • handle a wide range of standard distributions, including the multivariate normal;
  • calculate joint, marginal and conditional continuous distributions;
  • manipulate multivariate means, variances and covariances;
  • transform univariate and multivariate continuous random variables;
  • derive, manipulate and interpret likelihood functions, and find maximum likelihood estimators;
  • understand regression and ANOVA models as examples of linear models;
  • estimate parameters in a linear model;
  • make inferences about model parameters through appropriate model comparisons;
  • develop a 'best-fitting' model in a systematic and pragmatic way;
  • undertake model checking procedures through the use of residuals;
  • use R to implement methods covered in the course;
  • prepare a structured word processed report of the statistical analysis of an open-ended problem.

44 lectures, 8 tutorials, 3 practicals


One 2hr 30 minute exam (90%) and a practical project (10%).

Full syllabus

Univariate distribution theory (6 lectures)
Revision of sample spaces, events and random variables; distribution functions, probability functions, probability density functions; moments; random variables without a mean (Cauchy as example); discrete standard distributions: hypergeometric, negative binomial; revision of normal; gamma and beta functions; gamma (χ2 as special case) and beta distributions; visualising distributions in R; transformations of univariate random variables including monotonic case and non-monotonic examples.

Multivariate distribution theory (9 lectures)
Random vectors; multivariate p.d.f.s for continuous random vectors; p.d.f.s of marginal and conditional distributions; covariance and correlation; independence; conditional expectation and variance; transformations of multivariate p.d.f.s using Jacobians; applications of transformations including the t distribution and Box-Muller simulation of normal r.v.s; covariance matrices; linear transformations and their effect on covariance matrices; multivariate normal including matrix form of p.d.f.; linear transformations of the multivariate normal; conditional distributions of multivariate normal components.
Likelihood (5 lectures)
Data and random samples; models and parameters; definition of likelihood and examples; introduction to maximum likelihood estimation; log likelihood; one parameter MLE; two parameter MLE using Hessian, including MLE for Normal with unknown mean and variance; interval estimation using likelihood; hypothesis tests using likelihood.
Likelihood case studies (2 lectures)
Two or three applications of likelihood inference to case studies.
Linear models (22 lectures)
  • Matrix representation of a linear model: linear regression, polynomial regression, multiple regression and ANOVA models as examples of linear models.
  • Least squares estimation: least squares estimators in matrix notation; distributional properties of least squares estimators and the residual sum of squares.
  • Hypothesis testing: the F-test for comparing nested linear models; t-tests.
  • Prediction: confidence intervals and prediction intervals
  • Model checking: diagnostics using standardized residuals; transformations; R2.
  • Factor independent variables: ANCOVA and one-way and two-way ANOVA.
  • Fitting and analysing linear models using R.

Reading list

Type Author(s) Title Library Blackwells Amazon
B Draper and Smith Applied Regression Analysis 519.536 (D) Blackwells Amazon
B Faraway Linear Models with R 519.538 (F) Blackwells Amazon
B Freund, Miller and Miller John E. Freund’s Mathematical Statistics with Applications 519.5 (F) Blackwells Amazon
B Kleinbaum, Kupper, Muller and Nizam Applied Regression Analysis and Other Multivariable Methods 519.536 (A) Blackwells Amazon
B Mood, Graybill and Boes Introduction to the Theory of Statistics 519.5 (M) Blackwells Amazon

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

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

Timetable (semester 1)

Wed 12:00 - 12:50 tutorial   Hicks Lecture Theatre D
Mon 14:00 - 14:50 lecture   Diamond Building LT2
Tue 16:00 - 16:50 lecture   Dainton Building Lecture Theatre 1
Wed 12:00 - 12:50 tutorial (group 33) (weeks 2,4,6,9,11) Hicks Lecture Theatre D
Wed 12:00 - 12:50 tutorial (group 34) (weeks 2,4,6,9,11) Hicks Lecture Theatre 9
Thu 13:00 - 13:50 tutorial (group 35) (weeks 2,4,6,9,11) Hicks Lecture Theatre 9
Thu 13:00 - 13:50 tutorial (group 36) (weeks 2,4,6,9,11) Hicks Lecture Theatre 10
Thu 13:00 - 13:50 tutorial (group 37) (weeks 2,4,6,9,11) Arts Tower Lecture Theatre 5