Introduction to regression modelling in R

Mon, 19/06/2017 - 09:30 to Wed, 21/06/2017 - 17:00

June 19-21, 2017 
The core outcome from this course is to recognise that most statistical methods you use can be understood under a single framework, as special cases of (generalised) linear models. Learning statistical methods in a systematic way, instead of as a "cookbook" of different methods, enables you to take a systematic approach to key steps in analysis (like assumption checking) and to extend your skills to handle more complex situations you might encounter in the future (random factors, multivariate analysis, choosing between a set of competing models). 
This three-day short course is aimed at applied researchers with prior experience using R and familiar with introductory statistics tools - you should know about the t-test, linear regression, analysis of variance and know something about orthogonal and nested designs.  If you have not used R before, we strongly recommend you attend the Introduction to R course on 13th June.  If you need to revise introductory statistics material, you should attend the Introductory Statistics for Researchers course on 14-15th June prior to taking the regression course, which will take such material as assumed knowledge.

Eventbrite - Introduction to Regression Modelling in R

 Make sure you bring your own laptop! We will sort out internet access for you.

The following topics will be covered during the course:

Linear regression

Simple linear regression, including assumptions, influential observations and inference
Equivalence of two-sample t-test and linear regression
Linear models with multiple predictor variables
Multiple regression
Analysis of variance (ANOVA), including multiple comparisons
More linear models
Paired and blocked designs
Factorial experiments
Interactions in regression
Model selection
Information criteria (AIC, BIC)
Penalised estimation (LASSO)
Mixed effects models
Random effects
Linear mixed effects models
Inference for mixed models, including likelihood ratio tests, parametric bootstrap, hypothesis tests, confidence intervals
Correlated random effects
Wiggly models
Spline smoothers, including diagnostics and interactions
Generalised linear models (GLMs)
Examples of GLMs
Fitting GLMs and checking assumptions, including mean-variance relationship
Extensions - offsets, zero-inflated models
  • UNSW students $300
  • UNSW staff and other UNSW-affiliated people $600
  • People with no UNSW affiliation $1,500
Course fees include morning and afternoon teas and lunches on all days.
Registration in now open. Please click the button below.
Eventbrite - Introduction to Regression Modelling in R
Bring your own laptop computer.
NOTE: If you have not used R before, we strongly recommend you attend the Introduction to R course on June 13.

Registration closing date

Event details

Event open to all
Seat availability


Room TBD
Kensington Campus
UNSW Sydney

Key contact

Dr Nancy Briggs
9385 8220