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STAT300 Statistical Modelling

Credit Points 6
Offering Not offered in 2012
Intensive School(s) None
Supervised Exam There is no UNE Supervised Examination.
Pre-requisites STAT261 or candidature in a postgraduate award in the School of Environmental and Rural Science or School of Science and Technology
Co-requisites None
Restrictions None
Notes None
Combined Units None
Coordinator(s) Robert Murison (rmurison@une.edu.au)
Unit Description

The aim of the unit is to introduce trainee statisticians to a broad range of advanced techniques in statistics that they will encounter later as consultant statisticians to clients requiring advice and support.

Topics include stepwise regression, mixed models, generalised linear models, semi-parametric regression longitudinal data analysis, bootstrap and multivariate methods.
Materials Text information will be published prior to commencement of the teaching period.
Disclaimer Unit information may be subject to change prior to commencement of the teaching period.
Assessment Assessment information will be published prior to commencement of the teaching period.
Learning Outcomes (LO) Upon completion of this unit, students will be able to:
  1. classify statistical problems by solution method;
  2. produce a 'consulting report' which sets out the problem, analysis and interpretation in terms a client can understand;
  3. demonstrate proficiency in the statistical analysis of data via a computer package; and
  4. handle problems similar but not identical to those encountered in the unit.

Graduate Attributes (GA)
Attribute Taught Assessed Practised
1 Knowledge of a Discipline
Knowledge of the discipline is imparted by lecture, online material and by notes, and it is assessed by practical computer workshops, tutorials as well as by written assignments. 		True True True
2 Communication Skills
Students are required to explain statistical concepts and analysis in tutorials and computer workshops. Written assignments require students to interpret and explain results of analysis and to include informative conclusions. 		True True True
4 Information Literacy
Students are required to retrieve, process and assimilate information from a variety of sources including CD, internet, textbooks, study guides and journals. 		True True True
5 Life-Long Learning
Examples of the application of modern statistics in the sciences allows students to appreciate the need to continually update and build on their statistical knowledge. 		True
6 Problem Solving
Statistics is a fundamental tool in the scientific method. Students need to apply statistical methods to address research questions. 		True True True
7 Social Responsibility
Ethical issues of data collection, experimental design and reporting of results are discussed throughout the unit. 		True
   

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