Hat Matrix Diagonal (Leverage) The diagonal elements of the hat matrix are useful in detecting extreme points in the design space where they tend to have larger values. 1 If h ii>2p=n, then observation iis considered to be outlying in X. leverage or hat calculates the diagonal elements of the projection (“hat”) matrix. It is useful for investigating whether one or more observations are outlying with regard to their X values, and therefore might be excessively influencing the regression results.. If the leverages are constant (as is typically the case in a balanced aov situation) the plot uses factor level … There are three parts to this plot: First is the scatterplot of leverage values (got from statsmodels fitted model using get_influence().hat_matrix_diag) vs. standardized residuals. In statistics, the projection matrix (), sometimes also called the influence matrix or hat matrix (), maps the vector of response values (dependent variable values) to the vector of fitted values (or predicted values). 2 Moderate leverage if h ii2[0:2;0:5) and high leverage if h ii2[0:5;1]. Points that have high leverage and large residuals are particularly influential. pr(a,b) calculates Pr(a
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