Calculate accurate error variance for robust statistical models.
The estimated variance of errors is a foundational metric in regression analysis, measuring the dispersion of data points around the fitted regression line. It provides insight into the reliability of statistical models, helping analysts determine how well independent variables explain dependent outcomes.
The estimated variance of errors ($\sigma^2$ or $s^2$) is calculated using the residual sum of squares divided by the degrees of freedom:
$$s^2 = \frac{\sum (y_i - \hat{y}_i)^2}{n - k - 1}$$
Where $y_i$ represents actual values, $\hat{y}_i$ represents predicted values, $n$ is sample size, and $k$ denotes model parameters.
Select your preferred input method, enter your residual errors separated by commas or supply the residual sum of squares directly alongside sample parameters, then submit to view detailed variance metrics immediately.
What is a good error variance? Lower values indicate predictions cluster closely around the regression line, implying higher accuracy.
Why divide by degrees of freedom? Adjusting for parameters ensures unbiased variance estimation.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.