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Math

R² Coefficient of Determination Calculator

R² (coefficient of determination) is the standard goodness-of-fit metric for simple linear regression — a value between 0 and 1 telling you the proportion of variance in Y that X explains. Paste (x, y) pairs (any of comma, space, tab or semicolon delimiters); the tool fits the ordinary least-squares (OLS) line y = a + b·x and returns slope, intercept, R², Pearson r, SSE and RMSE — handy for lab reports, paper figures and teaching demos.

R² (coefficient of determination)

0.3929

Pearson r

0.6268

Regression line

y = 0.785 + 0.425·x

Slope b: 0.425
Intercept a: 0.785
Effective n: 5
RMSE: 0.748
Fitted via ordinary least squares (OLS) y = a + b·x; R² equals r² with intercept; clamped to [0, 1].

Formula

Regression coefficients: b = Σ(xᵢ − x̄)(yᵢ − ȳ) ⁄ Σ(xᵢ − x̄)² a = ȳ − b · x̄ Residuals and goodness of fit: ŷᵢ = a + b · xᵢ SSE = Σ(yᵢ − ŷᵢ)² SST = Σ(yᵢ − ȳ)² R² = 1 − SSE ⁄ SST (equals r² for simple regression with intercept) r = Σ(xᵢ − x̄)(yᵢ − ȳ) ⁄ √(Sxx · Syy) RMSE = √(SSE ⁄ n)

Frequently asked

What is the difference between R² and Pearson r?

Pearson r measures the direction and strength of linear association between two variables in [-1, 1] — positive means co-moving, negative means anti-moving. R² measures the proportion of variance explained by the model in [0, 1] — no direction. The relationship is simple: for simple linear regression with an intercept, R² = r² exactly, so r = 0.8 gives R² = 0.64 and r = −0.9 gives R² = 0.81. Practical rule: report r when you want to convey "strength + sign of a linear relationship"; report R² when you want to convey "how good is the model fit"; report Adjusted R² (not r) for multiple regression since r doesn't generalise. Another classic trap: correlation is not causation — even r = 1 doesn't imply X causes Y.

What counts as a "good" R²?

There's no universal cut-off — it depends on the field. In physics and engineering experiments (measurement instruments, linear devices) R² > 0.99 or even 0.999 is routine; 0.95 already counts as noisy. Social science, psychology and econometrics dealing with human behaviour routinely publish R² > 0.30; in equity-return forecasting R² < 0.05 is normal (efficient markets, low predictability). Biology and clinical research sit in between at 0.40 – 0.80. Practical rules: (1) compare to the literature in the same field; (2) consider sample size — high R² on small samples may be overfitting, validate with cross-validation; (3) combine with domain knowledge and the residual plot — high R² with a curved residual plot still misses non-linearity; low R² with random residuals means the model has done its job and the rest is noise.

Can R² be negative?

For OLS simple linear regression with an intercept, R² is mathematically guaranteed to lie in [0, 1] — because OLS's SSE is always ≤ SST (the worst case is slope = 0, predicting the constant ȳ, which gives SSE = SST and R² = 0). Two common situations show "negative R²": (1) regression without an intercept (forcing a = 0 strips the ȳ baseline, so the model can do worse than a constant predictor); (2) out-of-sample evaluation such as cross-validation — the test-set 1 − SSE/SST can go negative, meaning "you'd do better just predicting the training mean." This tool reports the in-sample OLS R² and clamps to [0, 1]. Seeing a negative R² in the wild almost always means there's something wrong with the model formulation that needs rechecking.

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