Linear Regression Simulator

Edit data points and simulate the best fit regression line

Data points, one pair per line

Linear Regression Formulas

Regression line	y = mx + b
Slope	m = sum((x-xbar)(y-ybar)) / sum((x-xbar)^2)
Intercept	b = ybar - m xbar
R-squared	R^2 = 1 - SSE / SST

Step-by-Step Example

The simulator calculates the mean of x and y, then uses deviations from those means to find the slope. After the slope is known, the intercept is b = ybar - m xbar. The plotted line shows the fitted prediction for y at each x-value.

Common Mistakes

Assuming correlation proves causation.
Using a straight line when the relationship is clearly curved.
Ignoring outliers that strongly pull the regression line.
Extrapolating far outside the observed x-range.

Frequently Asked Questions

What is linear regression?▼

Linear regression fits a straight line to data points to model the relationship between x and y.

What is the best fit line?▼

The best fit line is the line that minimizes the sum of squared vertical errors between the observed points and the line.

What does R-squared mean?▼

R-squared measures the proportion of variation in y explained by the linear relationship with x.

What does the slope mean?▼

The slope estimates how much y changes for a one-unit increase in x.

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