Robust Regression Calculator
Calculate robust regression (M-estimation) online to reduce the impact of outliers. Use Huber or Tukey loss functions for less sensitive regression coefficients.
Input Data
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Regression Settings
Regression Coefficients:
Predicted Values:
Residual Weights:
Visualization
About Robust Regression
Robust Regression is a statistical technique designed to mitigate the impact of outliers in regression analysis. Unlike ordinary least squares regression, which is highly sensitive to extreme values, robust regression methods aim to provide more stable and reliable estimates of regression coefficients in the presence of outliers. M-estimation is a common approach in robust regression, using loss functions that are less influenced by large residuals. Two popular loss functions are:
- Huber Loss: It is quadratic for small errors and linear for large errors, making it less sensitive to outliers than squared error loss.
- Tukey Loss (Bisquare): It reduces the weight of residuals as they become larger, effectively ignoring outliers beyond a certain point.
Robust regression is particularly useful in datasets where outliers are suspected to be present, ensuring that the regression model is not unduly influenced by these extreme values, leading to a more accurate representation of the underlying relationship between variables.