Calculadora de Error Estándar de Regresión Gratis
Calcula el error estándar de la estimación en regresión para medir la dispersión de los datos alrededor de la recta.
Standard Error of Regression
2.0628
Standard Error of Regression vs Residual Sum of Squares (SSE)
Fórmula
## Standard Error of Regression (RMSE) The standard error of regression (also called residual standard error or RMSE) estimates the standard deviation of the residuals. ### Formula **Se = sqrt(SSE / (n - p - 1))** where SSE is the sum of squared residuals, n is sample size, and p is the number of predictors. Smaller Se means better prediction accuracy. The denominator (n - p - 1) accounts for the degrees of freedom lost estimating the model parameters.
Ejemplo Resuelto
SSE = 200, n = 50, p = 2 predictors.
- 01df = 50 - 2 - 1 = 47
- 02MSE = 200 / 47 = 4.2553
- 03Se = sqrt(4.2553) = 2.0629
Preguntas Frecuentes
How do I interpret the standard error of regression?
It is in the same units as the response variable. Approximately 68% of data points fall within ±1 Se of the regression line, and 95% within ±2 Se. Smaller Se means more precise predictions.
Is Se the same as RMSE?
Nearly. RMSE divides by n, while Se divides by (n-p-1). For large samples, they are practically identical. Se is the unbiased estimator; RMSE from cross-validation is preferred for model selection.
How does Se relate to R²?
They measure complementary aspects: R² is the proportion of variance explained (relative measure), Se is the absolute prediction error (in original units). A model can have high R² but large Se if the total variance is large.
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