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The variance around the residuals can also be used to test whether the model vio- lates the assumption of homoscedasticity, that is, equal variance over the length of the regression model. Univariate outliers should be identified before fitting a model but multivariate outliers, if present, are identified once the model of best fit is obtained. Outliers that cause a poor fit degrade the predictive value of the regression model; however, this has to be balanced with loss of generalizability if the points are omitted. Multivariate outliers are data values that have an extreme value on a combination of explanatory variables and exert too much leverage and/or discrepancy (see Chapter 5). Data points with high leverage and low discrepancy have no effect on the regression line but tend to increase the R square value and reduce the standard errors. On the other hand, data points with low leverage and high discrepancy tend to influence the 238 Chapter 7 Histogram dependent variable: weight (kg) Mean = 1. Correlation and regression 239 Scatterplot dependent variable: weight (kg) 4 2 0 −2 −3 −2 − 1 2 3 Regression standardized predicted value Figure 7. Data points with both a high leverage and a high discrepancy influence the slope, the intercept and the R square value. Thus, a model that contains problematic data points with high leverage and/or high discrepancy values may not generalize well to the population. Multivariate outliers can be identified using Cook’s distances and leverage values as discussed in Chapter 5. Mahalanobis distances are evaluated using critical values of chi-square with degrees of freedom equal to the number of explanatory variables in the model. To adjust for the number of variables being tested, Mahalanobis distances are usually considered unacceptable at the P < 0. This is confirmed in the Residual Statistics table, which shows that the maximum Mahalanobis distance is 11. If multivariate outliers are detected they can be deleted but it is not reasonable to remove troublesome data points simply to improve the fit of the model. In addition, when one extreme data point is removed another may take its place so it is important to 240 Chapter 7 Mean = 2. Alternatively, the data can be transformed to reduce the influence of the multivariate outlier or the extreme data point can be re-coded to a less extreme value.
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