Swinburne University of Technology - Melbourne Australia
Future Students - Courses
Duration
Contact Hours
Campus
Prerequisite
Corequisite
One semester / teaching period
3 Hours per Week
Hawthorn, Online
HMS780 Multivariate Statistics Multivariate Statistics
Nil
Credit Points: 12.5 Credit Points
A unit of study in the Master of Science (Applied Statistics).
Aims To provide an introduction to more advanced modeling techniques, including nonlinear regression, ordinal and multinomial regression, logistic regression, multi-level regression analysis and survival analysis. Learning Objectives: After completing this unit of study you will be able to · To identify some advanced regression techniques commonly used in social and health research and to understand the assumptions underlying their use. · To apply these techniques to relevant situations using statistical packages (in particular SPSS) and to interpret the results of the analyses. · To develop the capacity to carry out and report independent statistical analyses, together with an awareness of the limitations involved in generalizing the results of such investigations
Classes are held in a computer laboratory and practical exercises are integrated with class teaching throughout the sessions.
Online Quizzes (10%), Assignments (40%), Exam (50%).
Topics will be chosen from regression methods; log-linear models for investigating relationships in categorical data, non-linear regression to handle data which does not satisfy the assumptions required in linear models, an introduction to multi-level modeling, logistic regression for binary, nominal and ordinal data and survival analysis.
HMS793: Advanced Topics in regression. D. Meyer (Available from the Swinburne Bookshop) Software: IBM SPSS Statistics and HLM Student Version (free)
Agresti, A (1990),Categorical Data Analysis, Wiley.Agresti, A (1996), An Introduction to Categorical Data Analysis, Wiley.Aiken, LS & West, SG (1991), Multiple Regression: testing and Interpreting Interactions, Sage. Dobson, AJ (1999), An Introduction to Generalized Linear Models, Second Edition.Dobson, A (1983), An Introduction to Statistical Modelling, Chapman and Hall, London.Fienberg, SE (1980), The Analysis of Cross-Classified Categorical Data, 2nd edn, MIT Press.Draper, NR & Smith, H (1998) Applied Regression Analysis, Wiley Series in Probability and Statistics, New York.Hosmer, D & Lemeshow, S (2000), Applied Logistic Regression (2nd Edition). Lattin, JM, Carroll, JD & Green PE (2001), Analyzing Multivariate Data, Duxbury.Montgomery, DC, Peck, EA, & Vining, GG (2001), Introduction to Linear Regression Analysis, 3rd edn. Multi-level modelling. Snijders, TAB, Bosker, RJ (2003), Multilevel Analysis : An Introduction to Basic and Advanced Multilevel Modeling.Leyland, AH, Goldstein, H (2001), Multilevel Modelling of Health Statistics, Wiley.Bryk, AS, Raudenbush, SW (1992), Hierarchical Linear Models : Applications and Data Analysis Methods.
Agresti, A (1990),Categorical Data Analysis, Wiley.Agresti, A (1996), An Introduction to Categorical Data Analysis, Wiley.Aiken, LS & West, SG (1991), Multiple Regression: testing and Interpreting Interactions, Sage. Dobson, AJ (1999), An Introduction to Generalized Linear Models, Second Edition.Dobson, A (1983), An Introduction to Statistical Modelling, Chapman and Hall, London.Fienberg, SE (1980), The Analysis of Cross-Classified Categorical Data, 2nd edn, MIT Press.Draper, NR & Smith, H (1998) Applied Regression Analysis, Wiley Series in Probability and Statistics, New York.Hosmer, D & Lemeshow, S (2000), Applied Logistic Regression (2nd Edition). Lattin, JM, Carroll, JD & Green PE (2001), Analyzing Multivariate Data, Duxbury.Montgomery, DC, Peck, EA, & Vining, GG (2001), Introduction to Linear Regression Analysis, 3rd edn.
Multi-level modelling. Snijders, TAB, Bosker, RJ (2003), Multilevel Analysis : An Introduction to Basic and Advanced Multilevel Modeling.Leyland, AH, Goldstein, H (2001), Multilevel Modelling of Health Statistics, Wiley.Bryk, AS, Raudenbush, SW (1992), Hierarchical Linear Models : Applications and Data Analysis Methods.
