Linear Regression Calculator

Enter X and Y data sets to find the best-fit line and make predictions.

X Values (comma-separated)

Y Values (comma-separated)

Predict y for x = (optional)

Regression Equation

y = 0x + 0

Slope (m)

Intercept (b)

r²

Correlation (r)

Predicted y

—

Linear Regression

Linear regression finds the best-fit line y = mx + b through a set of data points by minimizing the sum of squared residuals.

Slope: m = (nΣxy - ΣxΣy) / (nΣx² - (Σx)²)

Intercept: b = ȳ - m×x̄

Pearson correlation coefficient.

Mean, median, mode.

Spread of data.

Linear interpolation.

Statistical significance.

Chi-square test.