What pattern of residuals can be expected with negative autocorrelation?

In the context of statistical analysis and regression, negative autocorrelation indicates that there is a tendency for a positive residual to be followed by a negative residual, and vice versa. This means that when the error (or residual) of one observation is above the expected value (positive), the next error is likely to be below (negative), creating a pattern of alternating signs in the residuals.

This alternating pattern is a characteristic of negative autocorrelation, where the relationship between consecutive residuals is such that high errors are likely to be followed by low errors and low errors by high errors. The presence of negative autocorrelation can signal that there are underlying cycles or patterns in the data that have not been adequately modeled.

Understanding this concept is crucial for diagnosing potential issues in regression models and improving their accuracy by refining the analytical approach.