Autocorrelation in regression analysis refers to a situation where the residuals (the differences between the observed and predicted values) are correlated with each other over time. This means that the errors in one observation are related to the errors in another observation. For example, if you’re analyzing sales data over several months and find that the sales this month are similar to last month, this suggests autocorrelation. In simple terms, autocorrelation indicates that past errors can influence current errors, which can lead to inaccurate predictions and results in a regression model.