title: Understanding and checking the assumptions of linear regression: a primer for medical researchers.
abstract: Linear regression (LR) is a powerful statistical model when used correctly. Because the model is an approximation of the long-term sequence of any event, it requires assumptions to be made about the data it represents in order to remain appropriate. However, these assumptions are often misunderstood. We present the basic assumptions used in the LR model and offer a simple methodology for checking if they are satisfied prior to its use. In doing so, we aim to increase the effectiveness and appropriateness of LR in clinical research.
authors.affiliation: South Australian Institute of Ophthalmology, University of Adelaide, Adelaide, South Australia, Australia; Discipline of Ophthalmology & Visual Sciences, University of Adelaide, Adelaide, South Australia, Australia; Sight for All, Royal Adelaide Hospital, Adelaide, South Australia, Australia.
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Casson
authors.forename: Lachlan D M
authors.initials: LD
authors.lastname: Farmer
mesh_terms: D035843:Biomedical Research; D016014:Linear Models; D015233:Models, Statistical
keywords: assumption; normality; regression; statistics