Sometimes a set of observations may violate certain model assumptions (e.g., data follows multivariate normal distribution). These observations are called outliers. The plots explained in the previous section can be used to detect possible outliers in the multivariate data. If one or more points fall outside the majority of the points on the Q-Q plot, then those points are suspected to be outliers. However, it is known that the statistics and S are both sensitive to the presence of outliers. Hence the squared Mahalanobis distance d_{i}^{2} calculated using the formula d_{i}^{2} = (y_{i} - )′S^{−1}(y_{i} - ) may not ...

Start Free Trial

No credit card required