Drop a CSV, pick a predictor and an outcome, and fit a line — with R² and a plain-English reading of how good the fit is. Runs entirely in your browser: no upload, no sign-up, your data never leaves your device.
Linear regression fits the straight line y = slope × x + intercept that best predicts your outcome from your predictor. The slope is how much y changes per 1-unit change in x; the intercept is the predicted y when x is 0. R² runs from 0 to 1 and is the share of the variation in y the line explains. The p-value tests whether the slope differs from zero; below 0.05 is the usual “significant” threshold.
Simple linear regression assumes a roughly linear relationship and even scatter around the line. It’s sensitive to outliers, and a strong fit never proves x causes y. If the points look curved or the spread fans out, a straight line is the wrong model. Want multiple predictors or residual plots? Open the full workbench →