Like variance, mean squared error has the disadvantage of heavily weighting outliers. Some estimators are highly sensitive to outliers, notably estimation of covariance matrices. In the MAD, the deviations of a small number of outliers are irrelevant. One motivation is to produce statistical methods that are not unduly affected by outliers. Mahalanobis distance is also used to determine multivariate outliers. He used logistic regression, correlation, and smoothing, and improved the treatment of outliers. The interquartile range is often used to find outliers in data. Such estimators have some additional desirable properties, such as complete rejection of gross outliers. This approximation also suffers from the same problem as kurtosis (sensitivity to outliers). Naive interpretation of statistics derived from data sets that include outliers may be misleading. The significance of the outliers vary depending on the sample size. Therefore, it would be more likely to find data that are marked as outliers. Minimum &colon; the lowest data point excluding any outliers. Computing factor scores allows one to look for factor outliers. The data set contains two outliers, which greatly influence the sample mean.