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Linear Regression Calculator

Line of best fit, with r² and the standard error.

Work out Linear Regression. Line of best fit, with r² and the standard error. Shows the working, not just the answer.

Written and maintained by Mohit PatelLast checked August 4, 2026How we build these

Line of best fit

y = 1x + 1.3333

r² = 0.84 · very strong fit

Equationy = 1x + 1.3333
Slope1
Intercept1.333333
Correlation (r)0.916515
r² (variance explained)84%
Strengthvery strong positive
Data points6
Standard error0.91287

r² is the share of variation in y explained by x, and it is literally r squared — which is where over-claiming usually starts. A correlation of 0.92 sounds very strong, but it accounts for only 84% of the variation. The other 16% is something else. And none of this establishes causation: a strong fit is equally consistent with x causing y, y causing x, or a third factor driving both.

How the Linear Regression Calculator works

Least-squares regression finds the line minimising the squared vertical distances to your points. It returns the equation, r², and the standard error — which is the spread of the points around the line and the figure that tells you how much to trust a prediction.

Also known as: line of best fit calculator · least squares regression calculator · regression equation calculator · slope and intercept from data

Frequently asked questions

What is linear regression?

Fitting the straight line that best predicts one variable from another, by minimising the sum of squared vertical distances from the points to the line.

What does the slope mean?

How much y changes for each one-unit increase in x. A slope of 2 means y rises by 2 for every 1 that x rises.

What does r² tell me?

The share of variation in y explained by x. An r² of 0.49 means about half the variation is accounted for and half is something else.

Why least squares?

Squaring makes all errors positive and penalises large misses disproportionately, which yields a unique solution with a closed form. It is also sensitive to outliers for exactly the same reason.

Put this calculator on your own site

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The one-line version
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