Associate Lecturer - Advanced Statistics: Distribution Theory (HE1)

Job Description For all relevant UOL programmes ST2133 Advanced Statistics: Distribution Theory Uncertainty is part of life. If the outcomes of our actions were never in any doubt, life would be incredibly dull! Each of us possesses an intuitive sense that some events are more likely to occur than others. Science requires us to go beyond making purely vague statements such as ‘I can be pretty sure’ and ‘there are more likely outcomes’.

We need to quantify uncertainty by assigning numbers to possible events. Probability is the mechanism by which we quantify uncertainty, and distribution theory provides the tools which allow us to build probabilistic models of real-world phenomena Prerequisite If taken as part of a BSc degree, the following courses must be passed before this course may be attempted: MT1186 Mathematical Methods and ST1215 Introduction to Mathematical Statistics Aims and objectives  To recall a large number of distributions and be a competent user of their mass/density and distribution functions and moment generating functions.

 To explain relationships between variables, conditioning, independence and correlation.  To relate the theory and method taught in the unit to solve practical problem. Assessment This course is assessed by a three hour 15 minutes written examination (100%). Job Requirement A Ph.D or Master's Degree in related discipline from a reputable university.

Other requirements:

• At least 2 years of relevant teaching experience at the tertiary level is preferred - 5 years of relevant work experience will be an added advantage
• Applicant must be able to teach day time classes. We regret that only shortlisted candidates will be notified.