A scatterplot shows a cloud of points; regression draws the straight line through its middle — and that line is a prediction machine. The least-squares regression line, y = mx + b, is the unique line minimizing the total squared distance to every point. Its slope m says how much y changes per unit of x; its intercept b says where the line crosses the y-axis. For Texas homes, a regression of price on square footage might give price = 120 × sqft + 40,000: each extra square foot adds about $120, and the $40,000 intercept roughly captures the land value.

The line is only as honest as its fit. Extrapolating far beyond the data — predicting a 10,000-square-foot mansion's price from a model built on 1,500-square-foot homes — is where regression goes to embarrass people. And a line through curved data lies systematically, which is why checking the scatterplot first is non-negotiable.

This calculator evaluates the fundamental paired computation. Enter your x and y values in the Variable A and Variable B boxes, and the Result box returns their product — the term summed across all points to compute the slope m = Σ((x−x̄)(y−ȳ)) / Σ((x−x̄)²).

With the pre-filled defaults — Variable A 10, Variable B 5 — the Regression 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.