UWA Handbook 2017

Unit details

STAT4065 Multilevel and Mixed-Effects Modelling

Credit 6 points
  Offering
(see Timetable)
AvailabilityLocationMode
Semester 1UWA (Perth)Face to face
Content This unit examines the analysis of complex correlated data with a multi level or linear mixed model approach. Longitudinal (or repeated measures) data, clustered data and data from complex experimental designs are utilised to develop and describe methodology, and describe analyses approaches. The unit focuses on recognising data with these structures and identifying appropriate models to fit. It also addresses how to carry out such analyses using modern statistical techniques and packages, utilising real-world examples from many disciplines such as science (e.g. agricultural, biological, environmental, medical and physical), social sciences and finance.
Outcomes Students are able to (1) apply appropriate techniques from the above topics to real world data and communicate results in a logical and coherent fashion; (2) fit models to their data using modern statistical techniques and packages; (3) critically assess fitted models; and (4) interpret and communicate results of analysis of complex correlated data.
Assessment Typically this unit is assessed in the following ways: (1) assignments; (2) in-semester test; and (3) final examination. Further information is available in the unit outline.

Supplementary assessment is only available in this unit in the case of a student who has obtained a mark of 45 to 49 and is currently enrolled in this unit, and it is the only remaining unit that the student must pass in order to complete their course
Unit Coordinator(s) Associate Professor Berwin Turlach
Unit rules
Prerequisites: (STAT2401 Analysis of Experiments and STAT2402 Analysis of Observations) or STAT3405 Introduction to Bayesian Computing and Statistics
Incompatibility: STAT3401 Advanced Data Analysis
Contact hours lectures: 2 hours per week; labs: 2 hours per week
Unit Outlinehttp://www.unitoutlines.ecm.uwa.edu.au/Units/STAT4065/SEM-1/2017

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