This module is not currently running in 2024 to 2025.
R Package: This part will include a general introduction to the package and its components covering: linear models in R, writing your own functions in R, generalized linear models in R.
Further linear regression: Model selection, collinearity, outliers and influential observations, polynomial regression.
Generalized Linear Model: Exponential family; Discrete data distributions: definition, estimation and testing; GLMs: estimation, model selection and model checking; Examples of GLMs: logistic regression and Poisson regression; Overdispersion;
36
80% examination and 20% coursework.
Crawley, M. .J. (2009). The R Book, Wiley.
Draper, N. R. and Smith, H. (1998), Applied Regression Analysis, 3rd ed. Wiley.
Faraway, J. J. (2004). Linear Models with R, Chapman and Hall.
Faraway, J. J. (2006). Extending the Linear Model with R, Chapman and Hall.
McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models, 2nd ed, Chapman and Hall.
See the library reading list for this module (Canterbury)
On successful completion of this module students will:
a) be proficient in the use of the statistical package R;
b) be able to select suitable regression methods to analyse data in a sensible way and interpret the results appropriately;
c) be able to provide clear and competent reports on statistical analyses;
d) have a systematic understanding of linear and generalized linear modelling, and be able to apply these techniques critically to real world data using R;
e) be able to interpret the results of analyses, and communicate these clearly and concisely to other statisticians and to non-statisticians.
The intended generic learning outcomes
On successful completion of this module students will:
a) be able to plan and implement the analysis of unfamiliar material in a professional way;
b) be able to use information technology effectively for advanced data analysis;
c) have enhanced their computational skills in statistical modelling;
d) have developed a logical, mathematical approach to their work;
e) be able to appropriately manipulate data for regression analysis;
f) appreciate the need for techniques used to be appropriate to the type of data available.
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