Applied Statistical Modelling 1 - MA5501

Location Term Level Credits (ECTS) Current Convenor 2019-20
Canterbury Spring
View Timetable
5 15 (7.5) DR A Kume


Pre-requisite: MAST4009 (Probability); MAST4011 (Statistics); MAST4004 (Linear Algebra) or MAST4005 (Linear Mathematics); MAST4006 (Mathematical Methods 1); MAST4007 (Mathematical Methods 2)





Constructing suitable models for data is a key part of statistics. For example, we might want to model the yield of a chemical process in terms of the temperature and pressure of the process. Even if the temperature and pressure are fixed, there will be variation in the yield which motivates the use of a statistical model which includes a random component. In this module, we study how suitable models can be constructed, how to fit them to data and how suitable conclusions can be drawn. Both theoretical and practical aspects are covered, including the use of R.


This module appears in:

Contact hours

40 hours

Method of assessment

80% examination, 20% coursework

Indicative reading

Chatterjee, S., and Hadi, A.S. (2012) Regression analysis by example. 5th edition. Hoboken Wiley.
Draper, N. R., and Smith, H. (1998) Applied regression analysis. 3rd edition. Wiley.
Freedman, D. (2005) Statistical models: theory and practice. Cambridge University Press.

See the library reading list for this module (Canterbury)

Learning outcomes

The intended subject specific learning outcomes. On successfully completing the level 5 module students will be able to:

1 demonstrate knowledge and critical understanding of the well-established principles within statistical modelling using regression models and likelihood estimation;
2 demonstrate the capability to use a range of established techniques and a reasonable level of skill in calculation and manipulation to solve problems in the following areas: simple linear regression, linear models including estimation and diagnostics, one-way analysis of variance, maximum likelihood estimation, model selection strategies, estimation for the multivariate normal, partial and multiple correlation;
3 apply the concepts and principles in statistical modelling using regression models and likelihood estimation in well-defined contexts beyond those in which they were first studied, showing the ability to evaluate critically the appropriateness of different tools and techniques;
4 make appropriate use of R.

The intended generic learning outcomes. On successfully completing the level 5 module students will be able to:
Demonstrate an increased ability to:

1 manage their own learning and make use of appropriate resources;
2 understand logical arguments, identifying the assumptions made and the conclusions drawn;
3 communicate straightforward arguments and conclusions reasonably accurately and clearly;
4 manage their time and use their organisational skills to plan and implement efficient and effective modes of working;
5 solve problems relating to qualitative and quantitative information;
6 make use of R, online resources (Moodle), internet communication;
7 communicate technical and non-technical material competently.
8 demonstrate an increased level of skill in numeracy and computation.

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