A residual is the difference between the observed value and the predicted value in a statistical model. It represents the error or the part of the data that cannot be explained by the model, highlighting how well the model fits the data. In the context of iterative methods like conjugate gradient methods, residuals are crucial for evaluating convergence and adjusting approximations during optimization processes.
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