The Durbin-Watson test is a statistical method employed to assess the presence of autocorrelation in the residuals of a regression analysis. Autocorrelation arises when there is a systematic relationship between the errors of a regression model at different points in time, potentially leading to biased parameter estimates and inaccurate statistical inferences. Introduced by James Durbin and Geoffrey Watson, this test specifically focuses on detecting first-order autocorrelation, which refers to the correlation between adjacent residuals in a time series or panel data.