This course is designed for senior undergraduate and research students who need to design experiments and carry out statistical analysis of their data. Emphasis will be placed on the development of statistical concepts and statistical computing skills, rather than mathematical details. The content will be motivated by problem-solving in many diverse areas of application. The topics covered will include summarizing data by graphs and tables, the design and analysis of experiments, regression modeling with emphasis on model formulation, understanding the implications of model assumptions, diagnostic methods for model checking and interpretation, the analysis of binary and ordinal data and log-linear models for contingency tables.

To achieve an understanding of and facility in:

• Working knowledge of S-Plus (or R) statistical computing package.
• Fit simple and multiple linear regression models and interpret model parameters.
• Summarise and analyse relationships between a response variable and a covariate or covariates.
• Assess and refine simple and multiple linear regression models based on diagnostic measures.
• Carry out model selection in a multiple linear regression modelling context.
  The above outcomes for logistic and Poisson log-linear regression.
• To understand basic multivariate analyses techniques and the bootstrap.

• Assignments - 30%
• Final exam - 70%

Three hours of lectures and tutorials per week plus private study time.

STAT1008 Quantitative Research Methods or STAT1003 Statistical Techniques

Please refer to Course Website