The Durbin-Watson statistic tests for the presence of autocorrelation in the errors of a regression model. Autocorrelation means that the errors of adjacent observations are correlated.

Minitab provides two ways to determine whether residuals are correlated: Use a graph of residuals versus data order (1, 2, 3, 4, n) to visually inspect residuals for autocorrelation. A …

Anyone who has performed ordinary least squares (OLS) regression analysis knows that you need to check the residual plots in order to validate your model. Have you ever wondered …

Durbin-Watson statistic Tests for the presence of autocorrelation in residuals by determining whether or not the correlation between two adjacent error terms is zero. The test is based …

Overfitting a model is a real problem you need to beware of when performing regression analysis. An overfit model result in misleading regression coefficients, p-values, and R-squared …

Use el estadístico de Durbin-Watson para evaluar la presencia de autocorrelación. La autocorrelación significa que los errores de las observaciones adyacentes están …

Minitab calculates predicted R-squared by systematically removing each observation from the data set, estimating the regression equation, and determining how well the model predicts the …

Durbin-Watson Statistic Display the Durbin-Watson statistic to test for autocorrelation. For more information, go to Test for autocorrelation by using the Durbin-Watson statistic .

Use the autocorrelation function and the partial autocorrelation functions together to identify ARIMA models. Examine the spikes at each lag to determine whether they are significant. A …

Minitab performs two tests to determine whether the variances differ. Use Bartlett’s test if your data follow a normal, bell-shaped distribution. If your samples are small, or your data are not …