Math 385/585 Applied Regression Analysis

Fall 2011

Section 001 1:50 to 2:50, M W F

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

Classroom: Swart 203           Text: Applied Linear Statistical Models, 5th edition, by Kutner, Nachtsheim, Neter, and Li.  Link to Day by Day notes.

Catalog Description:  A practical introduction to regression emphasizing applications rather than theory.  Simple and multiple regression analysis, basic components of experimental design, and elementary model building.  Both conventional and computer techniques will be used in performing the analyses.  Prerequisite: Math 201 or Math 301 and Math 256 each with a grade of C or better.

Course Objectives:  The goal of statistics is to gain understanding from data.  This course focuses on critical thinking and active learning involving statistical regression.  Students will be engaged in statistical problem solving and will develop intuition concerning data analysis, including the use of appropriate technology.  Specifically students will develop

¥           an awareness of the nature and value of regression

¥           a sound, critical approach to interpreting statistics, including possible misuses

¥           facility with statistical calculations and evaluations, using appropriate technology

¥           effective written and oral communication skills

Grading: Final grades are based on these 300 points:

 

Topic

Points

Tentative Date

Chapters

Exam 1

Simple Linear Regression

70 pts.

October 7

1 to 4

Exam 2

Multiple Regression I

70 pts.

November 14

5 to 8

Exam 3

Multiple Regression II

70 pts.

December 16

9 to 11, 13 and 14

Homework

15 Points Each

90 pts.

 

 

 

Final grades are assigned as follows:

270 pts.           A (90 %)

260 pts.           A- (87 %)

250 pts.           B+ (83 %)

240 pts.           B (80 %)

230 pts.           B- (77 %)

220 pts.           C+ (73 %)

210 pts.           C (70 %)

200 pts.           C- (67 %)

190 pts.           D+ (63 %)

180 pts.           D (60 %)

179 pts. or less            F

 

Homework:  I will collect (around) 5 homework problems approximately once every other week.  The due dates are listed on the course outline below.  I suggest that you work together in small groups on the homework if you like, but don't forget that I am a resource for you to use.  Often we will use computer software to perform our analyses; include printouts where appropriate, but please make your papers readable.  In other words, I don't want 25 pages of printout handed in if you can summarize it in two.

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 2011 semester are 10:20 to 11:00, Monday, Wednesday, and Friday and 3:00 to 4:00, Thursday 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 digest the day's material.  That means you will benefit most by having read each section of the text before class.

My idea of education is definitely not "Teaching is telling and learning is listening".  I believe that you must be active in the learning process to learn effectively.  Therefore, I view my job as a teacher as not telling you the answers to the problems we will encounter, but rather pointing you in a direction that 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 resources to become adept at the material.  Remember, the goal is to learn mathematics, not to pass the exams.  (Incidentally, if you have truly learned the material, the test results will take care of themselves.)

Math 585 Expectations:  Expectations for the graduate students are understandably more rigorous than for the undergraduate student.  Students taking Math 585 will have an extra theoretical problem added to each homework, to be assigned during the semester.  In addition, a final project worth 50 points will be due at the end of the semester.  This project will involve a complete analysis of a data set, including model estimation, development, and validation.

Homework Assignments:  (subject to change if we discover difficulties as we go)

Homework 1, due September 21

Chapter 1:    1.19 p. 35
                    1.23 p. 36
                    1.33 p. 37
Chapter 2:    2.4 p. 90
                    2.55 p. 97

Homework 2, due October 5

Chapter 2:    2.23 p. 93
                    2.67 p. 99
Chapter 3:    3.3 p. 146-147
                    3.21 p. 151

Homework 3, due October 26

Chapter 3:    3.17 p. 150-151
Chapter 4:    4.21 p. 175
Chapter 5:    5.7 p. 210
                    5.20 p. 211
                    5.26 p. 212

Homework 4, due November 13

Chapter 6:    6.10 p. 249
Chapter 7:    7.4 p. 289
                    7.17 p. 290
Chapter 8:    8.16 p. 337-338
                    8.34 p. 340

Homework 5, due November 30

Chapter 9:    9.15 p. 378-379
                    9.16 p. 379
                    9.19 p. 379
Chapter 10: 10.10 (part a only) p 415

Homework 6, due December 14

Chapter 10:  10.10 b-f p. 415
Chapter 11:  11.29 p. 479
Chapter 13:  13.10 p. 550
                    13.12 p. 550

 

Monday

Wednesday

Friday

September 5
No Class

September 7 Day 1
Introduction, Least Squares

September 9 Day 2
Models
Sections 1.1 to 1.5

September 12 Day 3
Estimation
Sections 1.6 to 1.8

September 14 Day 4
 Inference
Sections 2.1 to 2.3

September 16 Day 5
Interval Estimates
Sections 2.4 to 2.6

September 19 Day 6
Homework 1 Due

ANOVA
Section 2.7

September 21 Day 7
GLM
Section 2.8

September 23 Day 8
Residuals I
Sections 3.1 to 3.6

September 26 Day 9
Residuals II
Sections 3.1 to 3.6

September 28 Day 10
 Lack of Fit
Section 3.7

September 30 Day 11
Transformations
Sections 3.8 to 3.9

October 3 Day 12
Simultaneous Inference
Homework 2 Due

Sections 4.1 to 4.3

October 5 Day 13
Review

October 7 Day 14
Exam 1

October 10 Day 15
Intro to Matrices
Sections 5.1 to 5.7

October 12 Day 16
Regression Matrices
Sections 5.8 to 5.13

October 14 Day 17
Mult. Reg. Models
Sections 6.1 to 6.2

October 17 Day 18
Inference
Sections 6.3 to 6.6

October 19 Day 19
Intervals
Section 6.7

October 21 Day 20
Diagnostics
Section 6.8

October 24 Day 21
Extra SS
Homework 3 Due

Section 7.1

October 26 Day 22
 GLM Tests
Sections 7.2 to 7.3

October 28 Day 23
Computational Problems and Multicollinearity
Sections 7.5 to 7.6

October 31 Day 24
Polynomial Models
Section 8.1

November 2 Day 25
Interactions I
Section 8.1

November 4 Day 26
Interactions II
Section 8.2

November 7 Day 27
Dummy Variables I
Sections 8.3 to 8.7

November 9 Day 28
 Dummy Variables II
Sections 8.3 to 8.7

November 11 Day 29
Homework 4 Due

Review

November 14 Day 30
Exam 2

November 16 Day 31
Model Building
Sections 9.1 to 9.3

November 18 Day 32
Best Subsets
Sections 9.4 to 9.6

November 21 Day 33
Diagnostics
Sections 10.1 to 10.2

November 23
No Class

November 25
No Class

November 28 Day 34
 X
Outliers
Section 10.3

November 30 Day 35
Homework 5 Due

Y
Outliers
Section 10.4

December 2 Day 36
Trees
Section 11.4

December 5 Day 37
Non-Linear Regression I
Sections 13.1 to 13.2

December 7 Day 38
Non-Linear Regression II
Sections 13.3 to 13.4

December 9 Day 39
Logistic Regression
Sections 14.2 to 14.3

December 12 Day 40
Homework 6 Due

Logistic Inference
Section 14.5

December 14 Day 41
Review

December 16 Day 42
Exam 3

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Last updated August 1, 2011