What is linear regression?

Linear regression finds the straight line (y = mx + b) that best fits a set of data points by minimizing the sum of squared vertical distances between each point and the line. The result is called the ordinary least squares (OLS) estimate.

What does R-squared mean?

R-squared (R²) is the coefficient of determination. It ranges from 0 to 1 and tells you what proportion of the variation in Y is explained by the linear relationship with X. An R² of 0.95 means 95% of the variance in Y is explained by the line.

How many data points do I need?

You need at least 2 points to define a line, but the regression is only statistically meaningful with 5 or more points. More data points generally give a more reliable estimate.

What format should I enter data in?

Enter one X,Y pair per line. You can separate X and Y with a comma, a space, or a tab. For example: "1,2" or "1 2" or "1\t2". Blank lines and lines starting with # are ignored.

Does this store my data?

No. All calculations happen entirely in your browser. Your data is never sent to any server.

Can I use negative numbers or decimals?

Yes. The calculator handles negative values and decimals in both X and Y columns.

Linear Regression Calculator

Name: Linear Regression Calculator
Availability: InStock
Author: Nham Vu

Enter X,Y data points to find the best-fit line equation, slope, intercept, and R-squared value — plus predict Y for any X.

Enter X, Y Data Points

Data (one pair per line)

Format: x, y or x y — at least 2 pairs required

Enter data on the left and click Calculate to see results.

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Summary

Enter X,Y data points to find the best-fit line equation, slope, intercept, and R-squared value — plus predict Y for any X.

How it works

Enter your data as X,Y pairs — one pair per line, separated by a comma or space.
Click "Calculate" to run the least-squares regression.
The tool displays the slope, intercept, and the full equation y = mx + b.
R-squared (R²) tells you the proportion of variance explained by the line (0–1; closer to 1 is a better fit).
Enter any X value in the prediction box to get the corresponding predicted Y.
Use "Copy Results" to copy all outputs to your clipboard.

Use cases

Analyze trends in sales or revenue data over time.
Predict future values based on historical data points.
Validate scientific measurements and experimental results.
Calculate grade curves or academic performance trends.
Estimate cost vs. volume relationships in business analysis.
Explore correlations between two numeric variables.
Homework help for statistics and data science courses.
Quick sanity-check before running full statistical software.

Frequently Asked Questions

Last updated: 2026-06-09 · Reviewed by Nham Vu