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STAT356 Linear Models

Credit Points 6
Offering
Responsible Campus Teaching Period Mode of Study
Armidale Trimester 2 Off Campus
Armidale Trimester 2 On Campus
Intensive School(s) None
Supervised Exam There is no UNE Supervised Examination.
Pre-requisites STAT261
Co-requisites None
Restrictions None
Notes

Two lectures and a one-hour laboratory session per week for on-campus students; off-campus students must have access to the statistical package R

Combined Units None
Coordinator(s) Robert Murison (rmurison@une.edu.au)
Unit Description

A balance between theoretical development and practical application will be maintained. Topics will include: model building, assessing the fit of the model, analysis of residuals; components of a generalised linear model, estimation, analysis of deviance, binary variables, logistic regression.

Materials Textbook information will be displayed approximately 8 weeks prior to the commencement of the teaching period. Please note that textbook requirements may vary from one teaching period to the next.
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. recognise an appropriate form of analysis for a data set, construct and fit a model, report the results;
  2. use exploratory data analysis to gauge systematic and random components in a data set;
  3. use the linear model to represent predictors of the response. Assess how much information can be attributed to each putative predictor;
  4. recognise that non-constant variance induces curvature in the predictor and use Generalised Linear Models to analyse such data; and
  5. understand the mathematical theory that underpins linear and generalised linear models.

Graduate Attributes (GA)
Attribute Taught Assessed Practised
1 Knowledge of a Discipline
Through learning statistical modelling graduates will recognise the attributes and limitations of different models.
True True True
2 Communication Skills
Graduates will communicate their results with formal setting out of solutions, augmented by clear sentences which explain the results and the meanings and importances of terms in the algebra. The explanation has to be relatively free of technical jargon and where such is necessary, its meaning be fully explained. Students will develop skills in drawing pen-pictures that explain features of graphs.
True True True
3 Global Perspectives
Graduates will appreciate that statistics is ubiquitous by learning how statistical techniques are generic and are driven by properties of the data rather than the application.
True True True
4 Information Literacy
Graduates will be equipped with terminology so that they can recognise statistical modelling in diverse settings such a science, business, social science, humanities, etc
True True True
5 Life-Long Learning
Solid foundations in modelling principles and the practice in analysing will allow graduates to confront a non-standard data analysis in any discipline.
True True True
6 Problem Solving
Graduates will be proficient in translating problems expressed in words to an algebraic formulation which allows analysis, performing the analysis mathematically, and expressing the solution in words in the context of the original problem.
True True True
   

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