A Practical Guide to Regression Models

For a complete overview of all statistical concepts and methods, return to the main guide: A Guide to Statistical Statistics.

Regression models are central to statistical analysis. They are essential whenever you want to quantify how one variable changes with another, predict an outcome, or control for multiple factors at once.

In practice, regressions are used whenever you want to explain, quantify, or predict an outcome variable based on one or more predictors. Here are examples of regression models you might have seen in the past :

Example of Regresion models

This series is an introduction to all my articles about regressions and related concepts. From a simple linear regression, to linear mixed models and more advanced concept, the goal is to create a useful ressource that you can revisit over and over while doing your own analysis.

Over time, more articles will be added to make this a comprehensive guide you can read and study to deepen your understanding of regressions!

Published

Statistical Test

1. Simple and Multiple Regression
2. Logistic Regression
3. Linear Mixed Model (part I : Theory)
4. Linear Mixed Model (Part II : Example)
5. Linear Mixed Models vs. Repeated-Measures ANOVA: How to Choose the Correct Model for Your Study

Related concepts

1. Comparing Correlation Techniques: Pearson, Spearman, Kendall Tau, and Point-Biserial

Bonus : Applied Regressions with fMRI data

1. Linear Regression with fMRI Data
2. First-Level and Second-Level Generalized Linear Model in fMRI