Advanced Topics in Regression
Expand your statistics skillset with advanced modeling techniques. Revive your survival analysis methods to measure and model events over time. You’ll learn several methods of regression analysis, and be able to apply them in a research setting.
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Subjects may require attendance
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Student who successfully complete this subject will be able to:
- Appraise how the type of data and the nature of the research questions affects the appropriate analysis methodology
- Contrast a range of advanced regression techniques, testing assumptions appropriately
- Assess logistic regression analyses for binary, ordinal and nominal response variables
- Contrast GEEs, Mixed and Multi-level Modelling analyses for nested and repeated measures data for nominal and metric response variables
- Formulate and report on appropriate Survival Analysis models using Cox Regression and Time Dependent Covariates
- Identify the most appropriate method of regression analysis in any particular research context.
- Multiple regression
- General linear model
- Generalised linear model
- Logistic regression for binary, nominal and ordinal data
- Generalised estimating equations and Mixed models
- Multi-level modelling
- Survival analysis
You cannot enrol in this subject if you have successfully completed any of the following subject(s) because they are considered academically equivalent:
You must have successfully completed the following subject(s) before starting this subject:
SWI-STA70002-Multivariate Statistics, or SWI-HMS780 ;
- OtherDetails -
Students require access to a recent version of SPSS - Grad Pak at the least.
To introduce more advanced modelling techniques, including ordinal and multinomial logistic regression, generalised linear models, GEEs, mixed models, multi-level models and survival analysis.
Please note: assessment values are indicative only, details will be advised at the start of the subject.
- Assignments — 2 (40%)
- Quizzes — Online (10%)
- Invigilated Exam (50%)