Linear regression finds the best-fit line y = mx + b that minimizes the sum of squared residuals. Slope m = (nΣxy−ΣxΣy)/(nΣx²−(Σx)²). Intercept b = ȳ − mx̄. R² measures goodness of fit (0 to 1). Pearson r = √R² with the sign of m.
How to Use
Enter data points:
- X values: Comma-separated
- Y values: Comma-separated
- Result: Best-fit equation
R² Interpretation
- R² = 1: perfect linear fit
- R² > 0.9: strong correlation
- R² > 0.7: moderate correlation
- R² < 0.3: weak correlation
Prediction
Once you have y=mx+b, plug in any x to predict y. Reliable within the data range; extrapolation beyond is less trustworthy.
Step-by-Step Instructions
- 1Enter x values.
- 2Enter y values.
- 3View best-fit equation.
- 4Check R² and correlation.
- 5Predict y for new x.