Average
Calculator
Mean, median, mode, range and standard deviation from any list.
Open the average calculator →Paste your paired X and Y values to get the least-squares line of best fit — slope, intercept, the y = mx + b equation and R-squared.
| Slope b₁ | — |
|---|---|
| Intercept b₀ | — |
| R squared fit quality | — |
| Correlation r | — |
| Pairs n | — |
Linear regression finds the straight line that best predicts Y from X by minimising the sum of the squared vertical distances from each point to the line — the least-squares method. Paste your paired data and the desk returns the line’s equation, its slope and intercept, and how well it fits.
The slope is how much Y changes for each one-unit rise in X; the intercept is the predicted Y when X is zero. The R² value, from 0 to 1, is the share of the variation in Y explained by the line — the closer to 1, the tighter the fit.
slope = Σ(x − x̄)(y − ȳ) ÷ Σ(x − x̄)² · intercept = ȳ − slope · x̄
The slope is the predicted change in Y for each one-unit increase in X. The intercept is the predicted value of Y when X equals zero. Together they define the line y = slope·x + intercept.
R² ranges from 0 to 1 and gives the fraction of the variation in Y explained by the line. What counts as “good” depends on the field — physical measurements may expect above 0.9, while noisy social data may treat 0.3 as meaningful.
Yes — plug an X into the equation to get a predicted Y. Be cautious predicting far outside the range of your data (extrapolation), where the linear pattern may no longer hold.
Mean, median, mode, range and standard deviation from any list.
Open the average calculator →Sample and population spread, variance and mean from any list.
Open the standard deviation calculator →Pearson’s r and r-squared from paired X and Y data.
Open the correlation calculator →A z-interval for the mean, with margin of error.
Open the confidence interval calculator →Take-home pay after federal, FICA and state tax in any state.
Open the paycheck calculator →