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)