What does "Residual Analysis" mean?
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Residual analysis is a method used to check how well a model fits a set of data. When we create a model to understand or predict something, like how machines work or how diseases spread, there will be some differences between what the model predicts and what actually happens. These differences are called residuals.
Why Residuals Matter
Residuals are important because they can show us if our model needs improvement. If the model is good, the residuals should be random and not show any patterns. If we notice patterns in the residuals, it might mean that our model is missing something or that we need to change it to get better results.
Analyzing Residuals
To analyze residuals, we look at their values and see how they behave. We can create graphs or use statistical methods to look for patterns. This helps us understand if our model is working well or if it needs adjustments.
Applications
Residual analysis can be used in many areas, including finance, engineering, and health sciences. By carefully studying the residuals, researchers and experts can improve their models, making them more effective for real-world situations.