A marketing team spends $50,000 on ads and revenue jumps $200,000 — but how much of that jump did the ads actually cause, and what would $60,000 buy? Regression analysis turns paired observations into that kind of answer. It finds the line (or curve) that best fits the cloud of data points, then reports how much of the outcome's movement the inputs explain. The headline number is R²: the share of variation accounted for by the model, from 0 (explains nothing) to 1 (explains everything).

Real analysis goes beyond the line itself. Analysts check residuals — the gaps between actual points and the line — for patterns that signal a bad fit, test whether each input's effect is statistically significant, and watch for multicollinearity, where two inputs move together so tightly the model cannot tell their effects apart. A model claiming each ad dollar returns exactly $4.00 deserves all three checks before anyone budgets on it.

This calculator performs the core paired computation behind the analysis. Enter the two variables under study in the Variable A and Variable B boxes — say, ad spend and revenue — and the Result box returns their combined product, the building block summed across observations to estimate the relationship's slope.

With the pre-filled defaults — Variable A 10, Variable B 5 — the Regression Analysis Calculator returns Result 50.0000. Change any input and those results update instantly, so you can see exactly how each number you enter moves the answer.