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Chapter 10

- Regression

REST OF CLASS INFO

- December 13 in TWO WEEKS is the date for our

project presentations. - The presentations should last approximately 15

minutes, and include visual aids. - The paper will be approximately 10 pages, and

that will include data/data analysis

Final exam and options

- To opt out of the final, take the grade average

of the grades that that the other students will

give you for the presentation. Since there are

22 total students, each student will give 8A,

8A-, 4B one of each grade, in other words to

each other presentations. A4.0, A- 3.7, and

B 3.3. Then I average your score. If you

like it, take it in place of the final exam.

Final Exam

- I will hold the final exam Thursday, December 20,

2001 at the same time/same place. - It will be a cooperative, open book effort.
- I will get some very interesting manufacturing

data.

Now for more stuff

- We have done enough factorials
- LINEAR REGRESSION is also a very useful topic,

and is Chapter 10 - This is my favorite topic, it should be right

after Chapter 2. - Instead of hypothesis testing, or estimating a

mean, realize that we could very well be

estimating the coefficients of the model

Model again

Plot

- In other words, we see that our Y or dependent

or response variable depends on a number of

predictor/regressor variables. - The idea find the equation of the line or

function

It can be more than first order type equation,

and have different type coefficients

Why we dont do by hand

- To solve by hand is a true nightmare, as it

involves solving matrix systems - Y XB error
- The ys, Xs, Bs, and error are all vectors. Y

is a singular vector, B is a square matrix, X is

N1 size columns, etc. - For example, the Bs which are very much of

interest are given in matrix notation by - B (XX)-1XY

The hard part

- Y XB e
- Where Y is a vector, B is a vector, X is a

matrix, and e is a vector - You need to solve for B

- http//www.stat.sc.edu/west/javahtml/Regression.h

tml

Last Day of Class, December 13

- PAPER/Project REMINDERS.
- Paper PLUS Presentation
- Your data/problem will suggest the types of

analysis. 3/4 methods eyeball, simple ANOVA,

factorial,..

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