Math 385 Applied Regression Analysis

Fall 2005

Section 001 8:00 to 9:00, M W F

Instructor: Dr. Chris Edwards Phone: 424-1358 or 948-3969 Office: Swart 123

Classroom: Swart 302 Text: Applied Linear Statistical Models, 5th edition, by Kutner, Nachtsheim, Neter, and Li.

Grading: Final grades are based on these 500 points:

 

Topic

Points

Tentative Date

Chapters

Exam 1

Simple Linear Regression

100 pts.

Oct. 5

1-4

Exam 2

Multiple Regression I

100 pts.

Nov. 11

5-8

Exam 3

Multiple Regression II

100 pts.

Dec. 12

9-11,13-14

Exam 4

Optional Final

100 pts.

Dec. 16

1-11,13-14

Homework

 

200 pts.

 

 

Final grades are assigned as follows:

450 pts. or more A (90 %)
425 pts. or more AB (85 %)
400 pts. or more B (80 %)
375 pts. or more BC (75 %)
350 pts. or more C (70 %)
325 pts. or more CD (65 %)
300 pts. or more D (60 %)
299 pts. or less F

Make-up exams will not be given. Exam 4, however, is an optional cumulative exam and will replace the lowest exam score. If any exam is missed for any reason, Exam 4 will replace that score.

Homework: There will be one homework assignment worth 20 points and six homework assignments each worth 30 points. Cooperation on homework is encouraged; copying is not. You are urged to work together on homework to solve problems; however, each of you must submit your own write-up.

Office Hours: Office hours are times when I will be in my office to help you. There are many other times when I am in my office. If I am in and not busy, I will be happy to help. My office hours for Fall 2005 semester are 3:00 to 4:00 Monday, Wednesday, and Friday, and 2:00 to 2:50 Tuesday, or by appointment.

Philosophy: I strongly believe that you, the student, are the only person who can make yourself learn. Therefore, whenever it is appropriate, I expect you to "discover" the mathematics we will be exploring. I do not feel that "lecturing" to you will teach you how to do mathematics. I hope to be your "guide" while we learn some mathematics, but you will need to do the learning. I expect each of you to come to class prepared to discuss the day’s material. That means you will have to pre-read each section of the text very carefully before class.

My idea of teaching / learning is not "Teaching is telling and learning is listening". I believe that you must be active in the learning process to learn well. My job as a teacher, therefore, is not to "tell" you the answers to the problems we will encounter; rather it is to point you in a direction which will allow you to see the solutions yourselves. To accomplish that goal, I will work to find different interactive activities for us to work on. Your job is to use me, your text, your friends, and any other sources as resources. Remember, the goal is to learn mathematics, not to pass the exam. (Incidentally, if you have truly learned the material, the test results will take care of themselves.)

Homework Assignments:

Homework 1, due Sep 23:

Chapter 1: 1.13, 1.21, 1.25, 1.38, 1.43

Chapter 2: 2.6 a-d

Homework 2, due Oct 3

Chapter 2: 2.25, 2.63, 2.66

Chapter 3: 3.5, 3.25

Homework 3, due Oct 14

Chapter 4: 4.4, 4.26

Chapter 5: 5.6, 5.17, 5.25, 5.28

Homework 4, due Oct 28

Chapter 6: 6.18, 6.19, 6.21, 6.29

Chapter 7: 7.7, 7.10, 7.19

Homework 5, due Nov 9

Chapter 8: 8.8, 8.24, 8.37, 8.39

Chapter 9: 9.6, 9.15

Homework 6, due Nov 28

Chapter 9: 9.16, 9.19

Chapter 10: 10.22 abdef

Chapter 11: 11.28

Homework 7, due Dec 9

(20 pts)

Chapter 13: 13.5, 13.7, 13.9

Chapter 14: 14.9

 

Monday

Wednesday

Friday

Sept 5
NO CLASS

Sept 7
Introduction, Least Squares

Sept 9
Models

Sept 12
Estimation

Sept 14
Inference

Sept 16
Inference

Sept 19
ANOVA

Sept 21
Residuals

Sept 23
Homework 1 Due
Lack of Fit

Sept 26
Transformations

Sept 28
Simultaneous Inference

Sept 30
Matrices

Oct 3
Homework 2 Due
Matrices

Oct 5
EXAM 1

Oct 7
Matrices

Oct 10
Multiple Regression

Oct 12
Multiple Regression

Oct 14
Homework 3 Due
Diagnostics

Oct 17
Extra SS

Oct 19
Extra SS

Oct 21
Multicollinearity

Oct 24
Multicollinearity

Oct 26
Polynomial Reg.

Oct 28
Homework 4 Due
Dummy Variables

Oct 31
Dummy Variables

Nov 2
Model Building

Nov 4
Model Building

Nov 7
Residuals

Nov 9
Homework 5 Due
Residuals

Nov 11
EXAM 2

Nov 14
Trees

Nov 16
Trees

Nov 18
Bootstrapping

Nov 21
Bootstrapping

Nov 23
NO CLASS

Nov 25
NO CLASS

Nov 28
Homework 6 Due
Non-Linear Reg.

Nov 30
Non-Linear Reg.

Dec 2
Non-Linear Reg.

Dec 5
Logistic Regression

Dec 7
Logistic Regression

Dec 9
Homework 7 Due

Dec 12
EXAM 3

Dec 14
Review

Dec 16
EXAM 4

 

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Managed by: Chris Edwards
edwards@uwosh.edu
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