Math 385/585 Applied Regression Analysis

Fall 2013

Section 001 10:20 to 11:20, M W F

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

Classroom: Swart 5   Text: Applied Linear Statistical Models, 5th 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

 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                     1.23 p. 36                     1.33 p. 37 Chapter 2:    2.4 p. 90                     2.55 p. 97 Homework 2, due September 30 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 21 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 8 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 22 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 9 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 2 No Class September 4 Day 1 Introduction, Least Squares September 6 Day 2 Models Sections 1.1 to 1.5 September 9 Day 3 Estimation Sections 1.6 to 1.8 September 11 Day 4  Inference Sections 2.1 to 2.3 September 13 Day 5 Interval Estimates Sections 2.4 to 2.6 September 16 Day 6 Homework 1 Due ANOVA Section 2.7 September 18 Day 7 GLM Section 2.8 September 20 Day 8 Residuals I Sections 3.1 to 3.6 September 23 Day 9 Residuals II Sections 3.1 to 3.6 September 25 Day 10  Lack of Fit Section 3.7 September 27 Day 11 Transformations Sections 3.8 to 3.9 September 30 Day 12 Homework 2 Due Simultaneous Inference Sections 4.1 to 4.3 October 2 Day 13 Review October 4 Day 14 Exam 1 October 7 Day 15 Intro to Matrices Sections 5.1 to 5.7 October 9 Day 16 Regression Matrices Sections 5.8 to 5.13 October 11 Day 17 Mult. Reg. Models Sections 6.1 to 6.2 October 14 Day 18 Inference Sections 6.3 to 6.6 October 16 Day 19 Intervals Section 6.7 October 18 Day 20 Diagnostics Section 6.8 October 21 Day 21 Homework 3 Due Extra SS Section 7.1 October 23 Day 22  GLM Tests Sections 7.2 to 7.3 October 25 Day 23 Computational Problems and Multicollinearity Sections 7.5 to 7.6 October 28 Day 24 Polynomial Models Section 8.1 October 30 Day 25 Interactions I Section 8.1 November 1 Day 26 Interactions II Section 8.2 November 4 Day 27 Dummy Variables I Sections 8.3 to 8.7 November 6 Day 28  Dummy Variables II Sections 8.3 to 8.7 November 8 Day 29 Homework 4 Due Review November 11 Day 30 Exam 2 November 13 Day 31 Model Building Sections 9.1 to 9.3 November 15 Day 32 Best Subsets Sections 9.4 to 9.6 November 18 Day 33 Diagnostics Sections 10.1 to 10.2 November 20 Day 34 X Outliers Section 10.3 November 22 Day 35 Homework 5 Due Y Outliers Section 10.4 November 25 Day 36 Trees Section 11.4 November 27 No Class November 29 No Class December 2 Day 37 Non-Linear Regression I Sections 13.1 to 13.2 December 4 Day 38 Non-Linear Regression II Sections 13.3 to 13.4 December 6 Day 39 Logistic Regression Sections 14.2 to 14.3 December 9 Day 40 Homework 6 Due Logistic Inference Section 14.5 December 11 Day 41 Review December 13 Day 42 Exam 3

Managed by: chris edwards

Last updated August 15, 2013