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Autocorrelation in time series data may be assessed using a variety of statistical approaches. The Durbin-Watson test is a typical method for determining the presence of first-order autocorrelation by examining the correlation of residuals at various delays. Another way is to plot the autocorrelation function (ACF) and partial autocorrelation function (PACF) to see correlation patterns at various lag intervals. Statistical techniques such as the Ljung-Box test and the Breusch-Godfrey test may also be used to measure overall autocorrelation in time series residuals. These tests serve to guarantee the validity of time series studies and highlight any necessary revisions to modelling methodologies.

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