If you have a scatter of data and want the straight line that best describes the trend, this calculator does exactly that. It uses linear regression (least squares) to compute the slope, intercept, correlation, and goodness of fit.
x, y or x y. Scientific notation is supported (e.g., 1e3).What is a best fit line?
A best fit line is a straight line that minimizes total error between observed data points and predicted values from the line. In simple linear regression, that line is written as:
y = m x + b
- m = slope (how much y changes for each 1-unit increase in x)
- b = intercept (predicted y when x = 0)
How this calculator works
This page computes a least-squares regression line using all points you enter. It also returns:
- R (correlation coefficient), indicating linear relationship strength and direction
- R² (coefficient of determination), showing fraction of variance explained by the model
- SSE (sum of squared errors), a raw error measure
- An optional prediction for a user-specified x value
Core formulas
For points (xᵢ, yᵢ), i = 1...n:
m = (nΣxy - ΣxΣy) / (nΣx² - (Σx)²)b = (Σy - mΣx) / nr = (nΣxy - ΣxΣy) / √[(nΣx²-(Σx)²)(nΣy²-(Σy)²)]R² = r²
How to use this best line fit calculator
Step 1: Paste your data
Enter one x-y pair per line. Example:
10, 100
20, 180
30, 250
Step 2: Run the calculation
Click Calculate Best Fit Line. You will immediately see the line equation and fit quality metrics.
Step 3 (optional): Predict a new value
Enter a new x value in the prediction field before calculating. The tool reports the estimated y on the fitted line.
Interpreting your results
- Positive slope: y increases as x increases.
- Negative slope: y decreases as x increases.
- R near 1 or -1: very strong linear relationship.
- R² near 1: the line explains most variability in y.
- Large SSE: points are farther from the fitted line on average.
Common mistakes to avoid
- Using non-numeric text inside a data row
- Providing fewer than two valid points
- Using identical x values for all rows (vertical line case; slope is undefined)
- Assuming linear regression proves causation—it only models association
When a line fit is useful
Best fit lines are practical in science labs, finance tracking, sales forecasting, quality control, and classroom statistics. If your scatterplot appears approximately linear, this method is often the simplest and most interpretable model.
FAQ
Does this calculator handle outliers?
It includes every data point equally in least squares. Strong outliers can significantly shift the fitted line.
Can I use decimals and negative values?
Yes. Decimals, negatives, and scientific notation are all supported.
Is this the same as polynomial regression?
No. This page computes a straight-line fit only (first-order linear regression).