Linear Regression Calculator
Fit a best-fit line from point data and compute r and R².
Linear Regression Calculator fits a straight-line model to paired x and y data. The slope describes the expected change in y for a one-unit increase in x, while the intercept is the predicted y value when x equals zero. Least-squares regression chooses the line that minimizes the sum of squared vertical residuals, where residuals are differences between observed and predicted y values. R-squared describes the share of variation in y explained by the linear model, but it does not prove causation or guarantee that predictions are valid outside the observed range. Regression is useful for trend estimation and calibration, provided the relationship is approximately linear, residual patterns are reasonable, and influential outliers are examined.