Regression Calculator

Linear regression turns a scatter of paired observations into a single line you can use to forecast and compare. Feed in your data points and the calculator returns the fitted equation along with the statistics that tell you how trustworthy it is. Whether you are weighing marketing spend against sales or tracking how interest rates move alongside loan defaults, that relationship is the kind of signal businesses lean on as they sharpen decisions heading into 2026.

The fit comes from the least squares method, which positions the line so the sum of the squared vertical distances between the points and the line is as small as possible. From that, the slope (b) is computed as Cov(x,y) / Var(x) and the intercept (a) as mean(y) - b * mean(x). The R-squared (R²) value then reports the share of variation in the dependent variable that the independent variable accounts for.

A handful of outliers can drag the line and inflate or deflate R² out of proportion to the bulk of your data, so it pays to inspect extreme points before trusting the output. Watch the interpretation, too: a strong R² confirms that two variables move together, not that one drives the other. Small or narrow datasets compound the problem, so the more observations and the wider their range, the more dependable the line becomes.