How Can Statistical Models be Validated in Catalysis?
Validation is crucial to ensure that the statistical models are reliable and accurate. Common validation techniques include: 1. Cross-Validation: Dividing the data into training and testing sets to evaluate model performance. 2. Residual Analysis: Examining the residuals or differences between observed and predicted values to assess model accuracy. 3. External Validation: Testing the model on a completely independent dataset to evaluate its generalizability.
