Least Squares Regression Line Calculator

Your details

Enter the independent (x) values separated by commas or spaces.
Enter the dependent (y) values in the same order as the x values.
Optional: enter any x-value to get the predicted y from the regression line.
Regression equationVery strong fit
y = 5.3690x + 47.7143

The least squares line in the form y = mx + b

Slope (m)5.369
Y-intercept (b)47.7143
Correlation (r)0.9983
R-squared (R²)0.9966
Std. error of estimate0.8321
Predicted y74.5595
Number of points (n)8
Sum of x36
Sum of y575
Sum of xy2,813
Sum of x²204
Residual SS4.1548
Total SS1,214.875
0.9966 R²
Very weak fit<0.25Weak fit0.25-0.5Moderate fit0.5-0.7Strong fit0.7-0.9Very strong0.9+

Regression line fitted to 8 points (R² = 99.7%).

  • The positive correlation (r = 0.9983) indicates a very strong linear relationship.
  • R-squared = 99.7%, meaning x explains 99.7% of the variance in y.
  • For each 1-unit increase in x, y changes by +5.3690 units on average.
  • Only 8 data points were used. More data improves the reliability of the regression line.

Next stepCheck residual plots to confirm that errors are randomly distributed before using this line for predictions.

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