Linear Regression Calculator

Calculate the best-fit line for your data points.

Regression Line

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Enter data points and calculate

Slope (m)

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Y-Intercept (b)

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Correlation (r)

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R-Squared (r²)

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Mean X

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Mean Y

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Std Error

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Predict Y

Linear Regression

Linear regression finds the best-fitting straight line through a set of data points by minimizing the sum of squared residuals.

Regression Equation

ŷ = mx + b

Where m is the slope and b is the y-intercept.

Formulas

m = Σ(x-x̄)(y-ȳ) / Σ(x-x̄)²
b = ȳ - m·x̄

Interpreting Results

Slope (m): Change in Y for each unit increase in X
R (correlation): Strength and direction of linear relationship (-1 to 1)
R²: Proportion of variance in Y explained by X (0 to 1)

Correlation Guidelines

\|r\| Value	Interpretation
0.90 - 1.00	Very strong
0.70 - 0.90	Strong
0.50 - 0.70	Moderate
0.30 - 0.50	Weak
0.00 - 0.30	Very weak/none

Frequently Asked Questions

What is the difference between correlation and regression?

Correlation measures the strength of the relationship between two variables. Regression describes that relationship with a mathematical equation, allowing predictions.

When is linear regression appropriate?

Linear regression is appropriate when there's a linear relationship between variables, residuals are normally distributed, and there's constant variance (homoscedasticity).