Regression

Test multivariate associations using regression techniques

Regression is a statistical technique used to understand multivariate associations between predictor, demographic, prognostic, clinical, confounding variables and outcome variables. The type of regression technique used depends upon the scale of measurement of the outcome variable. Click on a button below to learn more about that type of regression.

Types of regression

Regression analysis for a dichotomous categorical outcome.
Logistic Regression
Regression analysis for ​a polychotomous categorical outcome.
Multinomial Logistic Regression
Regression analysis for an ordinal outcome.
Proportional Odds Regression
Regression analysis for ​survival or time-to-event based on a dichotomous categorical outcome.
Cox Regression
Regression analysis for a continuous outcome.
Multiple Regression
Regression analysis for a count outcome where the mean is higher than the variance.
Poisson Regression
Regression analysis for a count outcome where the variance is higher than the mean.
Negative Binomial Regression
Use regression models to generate the probability of a disease state given known risk factors and provide a scoring system for clients and clinicians.
Nomograms

Methods of regression

There are three different methods of conducting a regression model. Different methods allow researchers to 1) control for confounding variables (simultaneous regression), 2) choose the best set of predictor variables that account for the most variance in an outcome (stepwise regression), or 3) test theoretical models (hierarchical regression). Click on a button below to learn more.
Simultaneous Regression
Stepwise Regression
Hierarchical Regression

Regression diagnostics

Regression diagnostics are absolutely necessary when conducting any of the aforementioned regression analyses. Click on a button below to learn more about each type of diagnostic test.
Residual Analysis
Normal Probability Plot
Linearity
Multicollinearity
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There are several types of regression: Logistic regression, multinomial logistic regression, proportional odds regression, Cox regression, multiple regression, simultaneous regression, stepwise regression, hierarchical regression, Poisson regression, and negative binomial regression.