The course introduces some statistical tools for regression analysis. It consists of lectures and computer based practicals, beginning with ordinary least squares and then developing other regression methods that allow the assumptions of ordinary least squared to be relaxed. The course is followed by a take home exam which covers both theoretical and practical aspects of the course.

After completing the course, students should be able to:

• Describe the estimator in ordinary least squares
• Explain the assumptions of ordinary least squares and the consequences of violating these assumptions
• Recognise when assumptions ordinary least squares are violated
• Choose appropriate regression technique given the properties ofthe data analysing data
• Interpret regression diagnostics and plots
• Build parsimonious models
• Make predictions with confidence intervals
• Analyses data in a modern statistical package
• Have some of the statistical skills necessary for their thesis projects