Statistics add-in software for statistical analysis in Excel
  • Statistical Reference Guide
  • Fit model
  • Linear fit

Residuals - independence

Autocorrelation occurs when the residuals are not independent of each other. That is, when the value of e[i+1] is not independent from e[i].

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.
Note: The p-value is computed using the bootstrap method and can take a long time to compute.
Related concepts
Residual plot
Residuals - normality
Outlier and influence plot
Related tasks
Plotting residuals
Available in Analyse-it Editions
Standard edition
Method Validation edition
Quality Control & Improvement edition
Ultimate edition

  •  What is Analyse-it?
  •  What's new?
  •  Administrator's Guide
  •  User's Guide
  •  Statistical Reference Guide
  •  Distribution
  •  Compare groups
  •  Compare pairs
  •  Contingency tables
  •  Correlation and association
  •  Principal component analysis (PCA)
  •  Factor analysis (FA)
  •  Item reliability
  •  Fit model
  •  Linear fit
  •  Simple regression models
  •  Fitting a simple linear regression
  •  Advanced models
  •  Fitting a multiple linear regression
  •  Performing ANOVA
  •  Performing 2-way or higher factorial ANOVA
  •  Performing ANCOVA
  •  Fitting an advanced linear model
  •  Scatter plot
  •  Summary of fit
  •  Parameter estimates
  •  Effect of model hypothesis test
  •  ANOVA table
  •  Predicted against actual Y plot
  •  Lack of Fit
  •  Effect of terms hypothesis test
  •  Effect leverage plot
  •  Effect means
  •  Plotting main effects and interactions
  •  Multiple comparisons
  •  Multiple comparison procedures
  •  Comparing effect means
  •  Residual plot
  •  Residuals - normality
  •  Residuals - independence
  •  Plotting residuals
  •  Outlier and influence plot
  •  Identifying outliers and other influential points
  •  Prediction
  •  Making predictions
  •  Making inverse predictions
  •  Saving variables
  •  Logistic / Probit fit
  •  Study design
  •  Method comparison / Agreement
  •  Measurement systems analysis (MSA)
  •  Reference interval
  •  Diagnostic performance
  •  Survival/Reliability
  •  Control charts
  •  Process capability
  •  Pareto analysis
  •  Study Designs
  •  Bibliography



Version 6.15
Published 18-Apr-2023
statistics software, statistical software for Excel
  • Products
  • Store 
  • Support
  • Blog
  • About us
  • Download trial
  •  Search
  •  Sign in
  •  Contact us
Analyse-it editions
  • Standard edition
  • Medical edition
  • Method Validation edition
  • Quality Control & Improvement edition
  • Ultimate edition

  • Blog  
  • About us
  • Contact us  
  • Privacy policy


Copyright 2026 Analyse-it Software, Ltd, Leeds, United Kingdom .
We use essential cookies to run the site, and optional analytics to improve the experience for visitors. For more information see our Privacy policy.