On successful completion of this course, students will be able:

1.To help students understand and use the principles of statistical modelling;
2.To provide a unified conceptual and theoretical framework for many of the most commonly used statistical methods including multiple linear regression, analysis of variance and logistic regression;
3.To develop skill in statistical computing.

Topics include:
. Linear models
. Model fitting as an approach to statistical analysis
. Exponential family of distributions
. Maximum likelihood estimation
. Inference methods based on model fitting
. Generalised Linear Models
. Models for continuous data (regression analysis of variance)
. Models for categorical data (logistic regression for nominal and ordinal data, Poisson regression and log-linear models)
. Generalised Additive models

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STAT2010 Fundamentals of Statistics
STAT2000 Applied statistics and Research Methods

Internal Mode

Lecture
Computer Lab

Lecture: for 2 hour(s) per Week for Full Term
Computer Lab: for 2 hour(s) per Week for Full Term