Regressionserkennung

Regressionserkennung, also known as anomaly detection in regression, is a technique used to identify data points that deviate significantly from the expected trend of a regression model. When a regression model is built to predict a continuous outcome variable based on one or more predictor variables, it establishes a general relationship. Regressionserkennung focuses on finding instances where the actual observed value is much different from the value predicted by the model.

This can occur due to various reasons, including measurement errors, unique events, or the presence of underlying factors not captured by the model. Identifying these outliers is crucial for several reasons. They might indicate data quality issues that need to be addressed, or they could represent genuinely interesting or important deviations that warrant further investigation. For example, in financial modeling, a regression model might predict stock prices, and regressionserkennung could flag unusually high or low prices that deviate from the predicted trend, potentially indicating market anomalies or insider activity.

Several methods exist for regressionserkennung. Common approaches involve analyzing the residuals, which are the differences between the observed and predicted values. Large residuals often suggest an outlier. Statistical tests, such as Z-scores or Cook's distance, can be employed to quantify the significance of these deviations. Visualizations like residual plots are also valuable tools for spotting patterns indicative of anomalies. The goal is to distinguish between random noise and meaningful deviations from the established regression relationship.