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Ch E 310 - Fall 10 - Lecture 16

# Ch E 310 - Fall 10 - Lecture 16 - Lecture 16 Agenda General...

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Lecture 16 October 19, 2010 Agenda: General Linear Least Squares Regression Linearizing equations (for those than can be) Non-linear Regression: fminsearch Homework 4 assigned (assemble groups of two)

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General Linear Regression Last time we talked about linear regression “General linear regression” refers to the model’s fitting parameters , not the model equation itself Examples: y = a 0 + a 1 x linear in a 0 and a 1 y = a 0 + a 1 x + a 2 x 2 linear in a 0 , a 1 , and a 2 y = a 0 + a 1 exp( x ) linear in a 0 and a 1 y = a 0 + a 1 exp( a 2 x ) non-linear (not valid) The previous method was specific for a linear function, but we can extend this to any arbitrary function for which the parameters are linear (general regression)