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Announcements
!
Our final exam is Thursday May 7 from 710pm (room assignments will be
announced prior to the final via email and the class web site).
!
If you have a time conflict with another exam (i.e. Calculus), you must bring
the proper form to me
personally
and I will add you to my list.
!
The last day to contact me personally about a makeup exam is Tuesday
April 21.
If you miss this deadline, you must take the exam during its
scheduled time.
!
The time of the makeup exam is Friday May 8 from 10  1pm.
Unless you
have a schedule conflict, you must take the exam during it’s regularly
scheduled time.
If this time does not work for someone with a conflict, you
must arrange a makeup with your other class.
!
During the week of April 27 May 1, there are no classes, discussions, stat
labs, or quizzes.
Review sessions begin Monday May 4.
Information
!
We can partition the total sum of squares into two
sources of variation.
!
If we are looking at the deviation between y
i
and ybar, it
can be split into two parts.
Question
!
How do the y
i
values vary around ybar?
!
Some of the difference is due to the difference between y
hat
i
and ybar.
"
This difference is accounted for by the difference between x
i
and xbar.
!
The rest of the difference is due to the difference between
y
i
and yhat
i
"
This difference is unexplained by the variation in x.
"
Represents variables not otherwise represented by the
model
Visualizing Errors in the Simple Model
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View Full DocumentUnderstanding Variation
!
Looking at the past equation as sums of squares…
We see
Variation in y = SSE + SSR
!
SSE measures the amount of variation in y that remains unexplained
!
SSR (SSM) measures the amount of variation in y that is explained by the
variation in the independent variable x.
Coefficient of determination,
r
2
The coefficient of determination,
r
2
,
square of the correlation
coefficient,
is the percentage of the variance in
y
(vertical scatter
from the regression line)
that can be explained by changes in
x
.
r
2
=
variation in
y
caused by
x
(i.e., the regression line)
total variation in observed
y
values around the mean
Procedure
1)
Develop a model theoretically and
set a response and
explanatory variable.
2)
Collect data for the two variables (try to conduct an experiment).
3)
Draw a scatter plot to see if a linear model is appropriate (also
consider correlation)
4)
Determine regression equation.
5)
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 Fall '08
 HOLT

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