The fits and diagnostics table displays observations that have a disproportionate impact on the regression model. These points are important to identify because they can produce misleading results.

Following are the are two types of influential observations:

·    Large residuals (R): These points are extreme in the y-direction relative to the fitted regression line. Standardized residuals that have absolute values greater than 2 are marked as large.

·    Leverage points (X): These points are extreme in the x-direction. If the leverage value is greater than 3 * number of model terms/number of observations, it is marked as a leverage point.

Influential observations do not follow the proposed regression equation well. However, some unusual observations are expected. For example, based on the criteria for large residuals, you would expect roughly 5% of your observations to be identified as having a large residual.

For influential observations, you should investigate whether the data are recorded correctly, and whether the data collection process was affected by any other factors. To determine the extent of influence, you can fit the model with and without the influential observations and compare the coefficients, p-values, R2, and other model summary values.

Observations 11, 43, and 44 are identified as unusual. You should investigate unusual observations to determine whether there is an identifiable problem.