h = leverage(data)
h = leverage(data,model)
'linear' - includes constant and linear terms
'interaction' - includes constant, linear, and cross product terms
'quadratic' - includes interactions and squared terms
'purequadratic' - includes constant, linear, and squared terms
Leverage is a measure of the influence of a given observation on a regression due to its location in the space of the inputs.
One rule of thumb is to compare the leverage to 2p/n where n is the number of observations and p is the number of parameters in the model. For the Hald data set this value is 0.7692.
load hald h = max(leverage(ingredients,'linear')) h = 0.7004
Since 0.7004 < 0.7692, there are no high leverage points using this rule.