# Stats Central - Online Short Course: Introduction to Regression Modelling in R, Aug 31-Sept 4

Facilities

### Course Overview

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 short course, taking place over five half-days, 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 learn basic features of R and how to use the RStudio interface to R before this course. If you have no experience with R, Stats Central offers an Introduction to R short course, which will be run on 11-12 August and 19-20 August (register HERE). If you need to revise introductory statistics material, you should attend the Introductory Statistics for Researchers course on 24-27 August (register HERE), as the material in it is taken as assumed knowledge for this regression course.

An outline of topics included in this regression course is below.

### Course Outline

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. ANCOVA. Factorial experiments. Interactions in regression.

Model selection — Cross-validation. 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.

Extensions — Spline smoothers, including diagnostics and interactions. Generalised linear models (GLMs).

Course Requirements: You will need to use a computer during the course. Some familiarity with introductory statistics and R will be assumed.

Date: Monday 31 August to Friday 4 September 2020 (five morning sessions)

Duration: 9.00am - 12.30pm each day 