Linear regression

Statistics

Proper linear model

Variables

Cross-sectional regression

Environmental health

Regression analysis

Regression dilution

Bayesian multivariate linear regression

Linear predictor function

Regression

Mathematics

Simple linear regression

Standardized coefficient

Econometrics

Factor regression model

Population

Regression diagnostic

Medicine

Analysis of covariance

Segmented regression

Poisson regression

Logistic regression

Life Cycle

Variables measured in medicine are usually continuous, such as blood pressure, serum glucose, and drug dosage. There are statistical methods to confirm whether these variables act as independent variables that cause an outcome. Most of these methods are types of regression analysis. In this article, starting with a simple correlation coefficient, linear regression, multi-factor regression analysis, analysis of covariance (ANCOVA), logistic regression, and modified Poisson regression will be explained. This article shall also cover when and how to use these regression analysis techniques. The following explanations can be used as a statistical background of research using regression analysis. It can also promote the understanding of medical researchers who would like to conduct advanced regression analysis.

Regression analysis for continuous independent variables in medical research: statistical standard and guideline of Life Cycle Committee