Analyze regression models and coefficients. Part of the DevTools Surf developer suite. Browse more tools in the Statistics collection.
Use Cases
Analyze which features most strongly predict customer churn from historical data.
Model the relationship between ad spend and revenue to estimate marketing ROI.
Identify price elasticity by regressing sales volume against price changes.
Validate that a linear model is appropriate before applying more complex ML methods.
Tips
Check residual plots before trusting R-squared — a high R-squared with non-random residuals means the model misspecifies the relationship.
Standardize features (z-score or min-max) before comparing coefficients — raw coefficients are not comparable when features have different scales.
Test for multicollinearity using VIF (Variance Inflation Factor) values above 5–10 indicate redundant predictors that destabilize coefficient estimates.
Fun Facts
The term 'regression' was coined by Francis Galton in 1886 to describe how extreme parental traits tend to move toward the population average in offspring — 'regression to mediocrity.'
Ordinary Least Squares (OLS) regression, the standard linear regression method, was developed independently by Carl Friedrich Gauss (1795) and Adrien-Marie Legendre (1805) — their priority dispute was one of the most famous in mathematics.
Linear regression is computationally trivial by modern standards but in 1950 fitting a regression on a 100-variable dataset required months of hand computation; the first computer-assisted regression analyses were performed in the 1950s at Bell Labs.
FAQ
What does R-squared actually mean?
R-squared (coefficient of determination) is the proportion of variance in the dependent variable explained by the model. 0.8 means 80% of variance is explained. It does not indicate whether the model's predictions are accurate in absolute terms.
When should I use logistic regression instead of linear regression?
Use logistic regression when the outcome variable is binary (yes/no, churned/retained). Linear regression applied to binary outcomes can predict probabilities outside [0,1] and is statistically inappropriate.
