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Final Project: Regression
Note #1:
There is an
openended final project option available to those who'd like a greater challenge
.
Note #2:
You may work with
one
partner
from your section
for this project if you wish. If you wish to
discuss the project with someone else, work with a partner. Otherwise, work alone. There is to be
NO
collaboration between individuals/groups who are not working as partners.
Theoretical Overview
Suppose we have a set of data consisting of ordered pairs and we suspect the
x
and
y
coordinates are related.
It is natural to try to find the best line that fits the data points. If we can find this line, then we can use it to
make all sorts of other predictions. In this project, we're going to use several functions to find this line using
a technique called
least squares regression
. The result will be what we call the
least squares regression
line
(or
LSRL
for short).
In order to do this, you'll be able to reuse some code you've already written (improve it if necessary, of
course), as the LSRL is more or less based on statistical calculations we've already automated. You'll need to
program one new statistical computation called the
correlation coefficient
, denoted by
r
in statistical
symbols:
Once you have the correlation coefficient, you use it along with the sample means and sample standard
deviations of the
x
and
y
coordinates to compute the slope and
y
intercept of your regression line via these
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 Fall '10

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