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RegressLab
The straight line that best predicts y from x.
Linear regression finds the line that minimizes the total squared vertical distance between the line and every data point — it's the 'best compromise' line through scattered data.
The slope tells you how much y changes for every 1-unit increase in x, and the intercept is the predicted y-value when x is zero — together they let you predict y for any new x value.
What you entered
Slope: m = Σ(x−x̄)(y−ȳ) ÷ Σ(x−x̄)²
least-squares fit= 1.99Intercept: b = ȳ − m·x̄
mean(y) − 1.99 × mean(x)= 0.05Regression line
y = m·x + b= y = 1.99x + 0.05Result
Regression line: y = 1.99x + 0.05
The best-fit line is y = 1.99x + 0.05 — for every 1-unit increase in x, y changes by about 1.99.