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STAT300 Statistical Modelling

Updated: 28 April 2010
Credit Points 6
Offering
Responsible Campus Teaching Period Mode of Study Online Level
Armidale Semester 2 Off Campus D - Comp/internet essential
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.
Referenced Material
Optional 		Text(s):

Note: Reference material is held in the University Library - purchase is optional

Data Analysis Using Regression and Multilevel/Hierarchial Models
ISBN: 9780521686891
Gelman, A. and Hill, J., Cambridge University Press 2006
Note: Available from the Dixson Library, UNE
Text refers to: Semester 2 , Off Campus
Disclaimer Unit information may be subject to change prior to commencement of the teaching period.
Assessment
Title Exam Length Weight Mode No. Words
Assignments 72%
Assessment Notes
12 assignments (1 per teaching week - 6% each)
Relates to Learning Outcomes (LO) and Graduate Attributes (GA)
LO: 1, 2, 3 GA: 1, 2, 4, 6
Practical 14%
Assessment Notes
Practical test
Relates to Learning Outcomes (LO) and Graduate Attributes (GA)
LO: 1, 2, 3, 4 GA: 1, 2, 4, 6
Project 14%
Assessment Notes
Mid-semester project
Relates to Learning Outcomes (LO) and Graduate Attributes (GA)
LO: 1, 2, 3, 4 GA: 1, 2, 4, 6

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;
  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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