Leverage

`h = leverage(data)`

h = leverage(data,* model*)

`h = leverage(data)`

finds
the leverage of each row (point) in the matrix `data`

for
a linear additive regression model.

`h = leverage(data,`

finds
the leverage on a regression, using a specified model type, where * model*)

`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.

[1] Goodall, C. R. "Computation Using
the QR Decomposition." *Handbook in Statistics.* Vol.
9, Amsterdam: Elsevier/North-Holland, 1993.

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