While a residual plot, or lag-1 plot allows you to visually check for autocorrelation, you can formally test the hypothesis using the Durbin-Watson test. The Durbin-Watson statistic is used to detect the presence of autocorrelation at lag 1 (or higher) in the residuals from a regression. The value of the test statistic lies between 0 and 4, small values indicate successive residuals are positively correlated. If the Durbin-Watson statistic is much less than 2, there is evidence of positive autocorrelation, if much greater than 2 evidence of negative autocorrelation.
The null hypothesis states that the residuals are not autocorrelated, against the alternative hypothesis that they are. If the test p-value is less than the predefined significance level, you can reject the null hypothesis and conclude the residuals are correlated. If the p-value is greater than the predefined significance level, you cannot reject the null hypothesis.
From the Statistical Reference Guide for Analyse-it 6.24.0: https://analyse-it.com/docs/user-guide/fit-model/linear/residual-auto-correlation
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