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, students study linear regression models (including estimation from data and drawing of conclusions), the use of likelihood to estimate models and its application in simple stochastic models. Both theoretical and practical aspects are covered, including the use of R.
Total contact hours: 40
Private study hours: 110
Total study hours: 150
Assessment 1 (10-15 hrs) 15%
Assessment 2 (10-15 hrs) 15%
Examination (2 hours) 70%
Level 6
Assessment 1 (10-15 hrs) 15%
Assessment 2 (10-15 hrs) 15%
Examination (2 hours) 70%
Reassessment methods:
Like-for-like
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)
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.
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