What does this regression calculator do?

It fits a simple linear regression (y = a + b x) to X–Y data and reports slope, intercept, correlation, R-squared, and a prediction for a chosen X.

How many points do I need?

At least 2 points are required, but 3 or more make regression meaningful.

R-squared is the proportion of variance in the dependent variable that is predictable from the independent variable.

Regression Calculator – Linear Regression, Correlation & Prediction

Regression Calculator

Enter X–Y pairs and this tool will run a simple linear regression of Y on X. You get the regression line (slope & intercept), the correlation coefficient, R², standard error, and a quick ANOVA table. You can also enter an X value to get a predicted Y.

Bulk data (X,Y per line)

#	X	Y
1
2
3

Predict Y for X =

Equation

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r (correlation)

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R²

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Predicted Y

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Blue dots: data points; Orange line: fitted regression line (first 60 points shown).

ANOVA (simple regression)

Source	SS	df	MS
Regression	—	—	—
Residual (Error)	—	—	—
Total	—	—	—

Formulas used

Regression line: \( y = a + bx \)

\( b = \dfrac{\sum (x_i - \bar{x})(y_i - \bar{y})}{\sum (x_i - \bar{x})^2} \)

\( a = \bar{y} - b\bar{x} \)

Correlation: \( r = \dfrac{\sum (x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum (x_i - \bar{x})^2 \sum (y_i - \bar{y})^2}} \)

R²: \( R^2 = 1 - \dfrac{\text{SSE}}{\text{SST}} \)

Interpreting results

r close to ±1 ⇒ strong linear relationship.
R² close to 1 ⇒ model explains most of the variability.
Check the scatterplot to ensure linearity – regression is a linear model.