Objectives: The course is designed to emphasise the principles of statistical modelling through the cycle of proposing a model, fitting it, criticising it, and modifying it. The use of graphical methods will also be stressed.

Introduction to S-Plus. The simple linear model: statistical inference for simple linear regression, estimation and model diagnostics; transformation models; interpretation of simple linear models. Multiple regression models: matrix formulation; estimation and diagnostics; outliers and measures of influence; variable selection. Models with indicator variables (including experimental designs).

To achieve an understanding of and facility in:

• Working knowledge of S-Plus statistical computing language, particularly graphical capabilities
• Fitting Simple Linear regression models and interpreting model parameters
• Summarising and analysing relationships between a response variable and a covariate
• Summarising and analysing relationships between a response variable and several covariates
• Assessing and refining simple and multiple linear regression models based on diagnostic measures. Identifying outlying and influential data points.
• Carrying out model selection in a multiple linear regression modelling context.

The following final assessment scheme is proposed:

• Assignments, 10%
• Midterm-exam, 25% 
• Three-hour final examination, 65%

Three contact hours per week plus private study time of at least 7 hours per week.

STAT1008 Quantitative Research Methods or STAT1003 Statistical Techniques

Please refer to Course Website