The ABC of linear regression analysis: What every author and editor should know

European Science Editing

article

Marko Lucijanic¹  Joan Marsh²  Ksenija Bazdaric³  Dina Sverko⁵  Ivan Salaric⁶  Anna Martinovic⁷ 

[1] Hematology Department, University Hospital Dubrava and Croatian Medical Journal;The Lancet/Elsevier;Rijeka University School of Medicine;European Science Editing and Croatian Medical Journal;Behavioral Health Home Rijeka;Department of Oral and Maxillofacial Surgery, University of Zagreb School of Dental Medicine, University Hospital Dubrava and Croatian Medical Journal;Department of English, University of Zadar

关键词: Causal language;    linear models;    prediction;    regression analysis;    reporting;    residuals;    statistics;   

DOI  :  10.3897/ese.2021.e63780

学科分类：工业工程学

来源: Pensoft

【 摘 要 】

Regression analysis is a widely used statistical technique to build a model from a set of data on two or more variables. Linear regression is based on linear correlation, and assumes that change in one variable is accompanied by a proportional change in another variable. Simple linear regression, or bivariate regression, is used for predicting the value of one variable from another variable (predictor); however, multiple linear regression, which enables us to analyse more than one predictor or variable, is more commonly used. This paper explains both simple and multiple linear regressions illustrated with an example of analysis and also discusses some common errors in presenting the results of regression, including inappropriate titles, causal language, inappropriate conclusions, and misinterpretation.

【 授权许可】

CC BY