Linear Regression Calculator

Linear regression is a statistical method that models the linear relationship between a dependent variable Y and an independent variable X using the equation y = ax + b.

The slope a indicates how much Y changes when X increases by one unit. A positive a means Y increases with X; a negative a means Y decreases.

The intercept b is the value of Y when X equals zero. It indicates where the regression line crosses the Y-axis.

R² (coefficient of determination) measures how well the regression model fits the data. A value of 1.0 means a perfect fit; 0.0 means no linear relationship.

It depends on the field. In natural sciences, R² > 0.95 is common. In social sciences, R² > 0.7 may be acceptable. Higher is generally better.

At least 2 points are needed mathematically, but 5–10 or more are recommended for reliable results. More data improves model stability.

Yes. The calculator accepts values separated by commas, semicolons, or spaces. Any consistent delimiter will work.

Extrapolating beyond the data range can be unreliable. The further from observed data, the greater the uncertainty. Use predictions with caution.

Correlation measures the strength of the linear relationship (r coefficient), while regression provides an equation for predicting Y from X. Both complement each other.

Avoid linear regression when the relationship between X and Y is non-linear. A low R² or curved residual plot signals that a different model may be more appropriate.