STA 121: Regression Analysis

STA 121: Regression Analysis

Instructor: Artin Armagan

217 Old Chemistry

668-5228

Time and Location: MF 10:05AM - 11:20AM / Social Sciences 228, W 10:05AM - 11:20AM / Old Chem 01.

Textbook: Terry E. Dielman, Applied Regression Analysis, 4th Edition, Duxbury.

Topics:

1 - Review

2 - Simple Linear Regression Analysis

3 - Multiple Regression Analysis

4 - Fitting Curves to Data

5 - Assessing the Assumptions of the Regression Model

6 - Using Indicator and Interaction Variables

7 - Variable Selection

8 - ANOVA

9 - Logistic Regression

10 - Introduction to Time-Series Analysis

Grading: Lab Assignments 20%

Exam 1 15%

Exam 2 15%

Final Exam 25%

Project 25%

Attendance to the labs are mandatory during which you will get started on the lab assignment. Attendance to the lectures is strongly encouraged to be able to keep up with the material.

Computation: We will be using R and/or JMP for computation. Statistics majors are strongly encouraged to get acclimated with R. Required R codes/commands will be provided.

Missed Graded Work: Make-ups are not given for missed work except for the final exam. Your final exam grade is substituted for missed work that is excused by the instructor. A grade of zero will be substituted for all other missed work.

Honor Code: As a citizen of the Duke University Community, each student is committed not to lie, cheat, or steal in her/his academic endeavors and to report known instances of violations to appropriate faculty. The full code is printed in the Undergraduate Bulletin. Violations of the honor code will be reprimanded by failure of this course and will be reported to the Undergraduate Judicial Board who may follow-up with suspension, disciplinary probation and/or other forms of restitution. Ignorance of what constitutes academic dishonesty is not a justifiable excuse for violations.

Resources: Statistical Education and Consulting Center (SECC) will have teaching assistants covering help sessions for 20-30 hours per week. See www.stat.duke.edu/secc for hours of operation. Use all the resources you need, but remember, the most important resource you have is you. Read the text, attend lectures, and work as many problems as you can. You are expected to put in about 6 hours of work outside of class. A few of you will do well with less time than this, and a few of you will need more.