Math
385/585 Applied Regression Analysis
Fall 2013
Section 001 10:20 to 11:20, M
W F
Instructor: Dr. Chris Edwards Phone: 4241358 or 9483969 Office: Swart 123
Classroom: Swart 5 Text: Applied Linear
Statistical Models, 5^{th} edition, by Kutner,
Nachtsheim, Neter, and
Li. Earlier editions of the
text will likely be adequate, but you will have to allow for different page
numbers and homework problem numbers. 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 4 
1 to 4 
Exam 2 
Multiple Regression I 
70 pts. 
November 11 
5 to 8 
Exam 3 
Multiple Regression II 
70 pts. 
December 13 
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 donÕt 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
donÕt 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 2013 semester are Tuesday 10:30 to 11:30, Wednesday 3:00 to
4:00, 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 dayÕs material. That means you will benefit most by
having read each section of the text and the Day By Day
notes before class.
My idea of education is that one learns by
doing. I believe that you must be
engaged in the learning process to learn well. Therefore, I view my job as a teacher not
as 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 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.
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 16 
Chapter 1: 1.19 p. 35 
Homework 2, due September 30 
Chapter 2: 2.23 p. 93 
Homework 3, due October 21 
Chapter 3: 3.17 p. 150151 
Homework 4, due November 8 
Chapter 6: 6.10 p. 249 
Homework 5, due November 22 
Chapter 9: 9.15 p. 378379 
Homework 6, due December 9 
Chapter 10: 10.10 bf p. 415 
Monday 
Wednesday 
Friday 
September 2 
September 4 Day 1 
September 6 Day 2 
September 9 Day 3 
September 11 Day 4 
September 13 Day 5 
September 16 Day 6 
September 18 Day 7 
September 20 Day 8 
September 23 Day 9 
September 25 Day 10 
September 27 Day 11 
September 30 Day 12 
October 2 Day 13 
October 4 Day 14 
October 7 Day 15 
October 9 Day 16 
October 11 Day 17 
October 14 Day 18 
October 16 Day 19 
October 18 Day 20 
October 21 Day 21 
October 23 Day 22 
October 25 Day 23 
October 28 Day 24 
October 30 Day 25 
November 1 Day 26 
November 4 Day 27 
November 6 Day 28 
November 8 Day 29 
November 11 Day 30 
November 13 Day 31 
November 15 Day 32 
November 18 Day 33 
November 20 Day 34 
November 22 Day 35 
November 25 Day 36 
November 27 
November 29 
December 2 Day 37 
December 4 Day 38 
December 6 Day 39 
December 9 Day 40 
December 11 Day 41 
December 13 Day 42 
Managed
by: chris
edwards
Last updated August 15, 2013