Which approach can be used to test for autocorrelation?

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BlackTom AI · Accepted Answer

To answer the question about testing for autocorrelation, we should evaluate what each option measures or tests for.
Option 1: 'regression coefficient' pertains to the estimated slope or effect size in a regression model. While significance tests on coefficients exist (like t-tests), this does not test whether residuals are autocorrelated over time or sequence, so it does not address autocorrelation directly.
Option 2: 'F-test or t-test' are general statistical tests used for evaluating hypotheses about parameters or model fit, but they are not specific tests for autocorrelation in the residuals. They can be part of regression analysis for other purposes, yet they do not diagnose correlation between consecutive error terms.
Option 3: 'Durbin-Watson statistic' is specifically designed to detect autocorrelation in the residuals from a regression analysis, particularly first-order autocorrelation. It assesses whether adjacent residuals are correlated, which is the hallmark of autocorrelation concerns in time-series or ordered data.
Option 4: 'correlation coefficient' measures the strength and direction of a linear relationship between two variables. While correlation concepts are related, this option does not directly test for autocorrelation of residuals within a regression context, making it unsuitable for diagnosing autocorrelation in regression residuals.
In summary, the Durbin-Watson statistic is the tool tailored for detecting autocorrelation in residuals, whereas the other options address different aspects of regression analysis or relationships between variables and do not specifically test for residual autocorrelation.

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Which approach can be used to test for autocorrelation?

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