title: Introduction to statistical modelling 2: categorical variables and interactions in linear regression.
abstract: In the first article in this series we explored the use of linear regression to predict an outcome variable from a number of predictive factors. It assumed that the predictive factors were measured on an interval scale. However, this article shows how categorical variables can also be included in a linear regression model, enabling predictions to be made separately for different groups and allowing for testing the hypothesis that the outcome differs between groups. The use of interaction terms to measure whether the effect of a particular predictor variable differs between groups is also explained. An alternative approach to testing the difference between groups of the effect of a given predictor, which consists of measuring the effect in each group separately and seeing whether the statistical significance differs between the groups, is shown to be misleading.
authors.affiliation: Arthritis Research UK Epidemiology Unit, Centre for Musculoskeletal Research, Institute of Inflammation and Repair, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK mark.lunt@manchester.ac.uk.
authors.forename: Mark
authors.initials: M
authors.lastname: Lunt
mesh_terms: D015535:Arthritis, Psoriatic / Q000145:classification / Q000208:ethnology / Q000503:physiopathology; D001172:Arthritis, Rheumatoid / Q000145:classification / Q000208:ethnology / Q000503:physiopathology; D015992:Body Mass Index; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D011237:Predictive Value of Tests
keywords: categorical variable; dummy variable; indicator variable; interaction; linear regression