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📐 Linear Regression Calculator

Fit a linear regression line to (x, y) data, and see the slope, intercept, correlation coefficient, R², and predicted values from the best-fit line.

Linear Regression Calculator

Paste paired (x, y) data and fit a least‑squares regression line. See the slope, intercept, correlation, R², and predicted values from the best‑fit line.

Data

One pair per line, e.g. 1 2 or 1,2

The calculator will ignore lines that do not contain two numeric values.

linear regression calculatorbest fit line & correlation calculator

Regression summary

Regression equation

ŷ = 1.45 + 0.75x

Slope (b₁)

0.75

Intercept (b₀)

1.45

Correlation (r)

0.984798

Coefficient of determination (R²)

0.969828

Prediction

x value to predict for

The prediction uses the best‑fit line and assumes a linear relationship continues at this x value.

Predicted value

ŷ at x

5.95

How to use this tool

Paste your (x, y) pairs, one per line.
Review the regression equation, slope, intercept, correlation, and R².
Enter an x value to see the predicted y from the best‑fit line.

Best fit line calculator for quick analysis

Turn a small data set into a linear trend line in seconds.
Use the slope and intercept in spreadsheets, reports, or exam questions.
Check how strong the linear relationship is using r and R².

What this linear regression calculator assumes

The relationship between x and y is approximately linear.
The calculation uses ordinary least squares with no extra weighting.
The tool does not check residuals or outliers, so always interpret results in context.

FAQ

Can I swap x and y?: Yes, but remember that regressing y on x is not the same as regressing x on y. Decide which variable should be treated as the predictor and which as the response.
What does R² tell me?: R² (the coefficient of determination) tells you what proportion of the variation in y is explained by the linear relationship with x.
Is this suitable for large data sets?: It works best for small to moderate data sets you might see in homework, projects, or quick analyses. For very large data, a dedicated stats package or spreadsheet may be better.

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