Fall 2019 Course Syllabus:  Math 644

 

Course Title:

Regression Analysis Methods

Textbook:

Applied Linear Regression, by Sanford Weisberg; Publisher: Wiley; 4th edition, 2014; ISBN: 9781118386088.

 

Reference Books:

Regression Analysis by Example, by Samprit Chatterjee and Ali S. Hadi (2012, 5th edition). Linear Models with R, by Julian Faraway (2005).          

Prerequisites:

Math 661 or equivalent with Departmental approval.

 

 

Course Outline

Date

Lecture

Chapter

Topic

Assignment

Week 1

9/9

1

 

Chapter 1

Scatterplots and Regression: scatterplots, mean functions, variance functions,

scatterplot matrices.

 

Week 2

9/16

2

Chapter 2

Simple Linear Regression: least square estimates, analysis of variance, coefficient of determination, confidence intervals and tests.

 

Homework 1

Week 3

9/23

3

Chapter 3

 

Multiple Regression: least square estimates, analysis of variance, prediction and

fitted values.

 

Week 4

9/30

4

Chapter 4

 

Interpretation of Main Effects: understanding parameter estimates, more on R squared, dropping regressors.

 

Homework 2

Week 5

10/7

5

Chapter 5

Complex Regressors: factors, many factors, polynomial regression, splines, principal components, missing data.

 

Week 6

10/14

6

Chapter 6

Testing and Analysis of Variance: analysis of variance, comparisons of means, Wald test,
interpreting tests.

 

Homework 3

Week 7

10/21

7

Chapter 7

Variances: weighted least squares, mis-specified variances, mixed models, delta method, bootstrap.

 

Week 8

10/28

 

 

MIDTERM EXAM:

Monday ~ October 28, 2019

 

 

Week 9

11/4

8

Chapter 8

Transformations: power transformations, Box-Cox method, general transformation methods,
additive models.

 

Regression Analysis Project

Homework 4

Week 10

11/11

9

Chapter 9

Regression Diagnosis: residuals, curvature, non-constant variance, outliers, influence of cases, normality assumption.

 

Week 11

11/18

10

Chapter 10

Variable Selection: stepwise regression, regularized methods, cross-validation.

 

Homework 5

Week 12

11/25

11

Chapter 11

Nonlinear Regression: estimation and inference for nonlinear mean functions.

 

Week 13

12/2

12

Chapter 12

        Binomial and Poisson Regression: Logistic regression, Poisson regression, generalized linear models.

 

 Homework 6

Week 14

12/9

 

 

Students’ Project Presentation

Week 15

12/16

 

 

FINAL EXAM:

Monday ~ December 16, 2019

 

 

 

 

IMPORTANT DATES

FIRST DAY OF SEMESTER

September 3, 2019

LAST DAY TO WITHDRAW

November 11, 2019

LAST DAY OF CLASSES

December 11, 2019

FINAL EXAM PERIOD

December 14 – 20, 2019

 

Grading Policy

 

Assignment Weighting

 

Tentative Grading Scale

Homework

25 %

 

A

90 -- 100

Project

15 %

 

B+

85 -- 90

Midterm Exam

25 %

 

B

80 -- 85

Final Exam

35 %

 

C+

75 -- 80

 

 

 

C

70 -- 75

 

 

 

F

0 -- 70

 

 

 

Important Departmental and University Policies

 

 

Prepared by Prof. Wenge Guo, August 8, 2019