Course Assignments:
 

Chapter	Topics
1	Linear Regression with One Independent Variable
2	Inferences in Regression Analysis
3	Diagnostics and Remedial Measures
4	Simultaneous Inferences and Other Topics in Regression Analysis
5	Matrix Approach to Simple Linear Regression Analysis
6	Multiple Regression I
7	Multiple Regression II
8	Building the Regression Model I: Selection of Predictor Variables
9	Building the Regression Model II: Diagnostics
10	Building the Regression Model III: Remedial Measures and Validation
11	Qualitative Predictor Variables
14	Logistic Regression, Poisson Regression, and Generalized Linear Models

Grading Policy:

     Two tests --------------------------------- 50%
     Homework & projects -------------------  25%
     Final Exam -------------------------------  25%
     Total   ------------------------------------100%

Final Grade:

     90% to 100% ----------------- A
     80% to below 90% -- at least a B
     70% to below 80% -- at least a C
     60% to below 70% -- at least a D
     below 60% ---------- at most a D

• Make-up tests will only be given to student who misses a test due to an extreme emergency and has notified the instructor within 24 hours after the exam, or a sufficient time period before the exam.  The student will be expected to provide verification, such as signed statement and phone number, to verify the reason for his or her absence from the exam.
• If you are having trouble, please see me right away.  I recommend that you work on the homework corresponding to the latest lecture as soon as it is assigned.  Successful students have found that the subject demands frequent intensive study with lots of problem solving.
• It is expected that you will make every possible effort to attend all classes.  In boarder line situations, class attendance may affect a student's grade.  However, no direct punishment will be assessed to your final grade if you choose not to attend class.  Students who missed class are responsible for finding out any pertinent information concerning the course from the instructor.
• If you have any special problem or learning difficulties, please see me immediately.

The last day to drop the course with a grade of "W" is Tuesday, November 3, 1998.

Homework & Projects:

Project 1:  Grade Prediction (SAS)
Project 2:  Visitor Guidance via Statistics (SPSS)
Project 3:  Social Data  (SPSS)
Project 4:  Modeling Demand Function (SPSS)
Project 5:  Consulting for Airlines (SAS)
Project 6:  Weighted Least Square Project (SPSS)
Project 7:  Modeling with Qualitative Predictor Variables (SPSS)
Project 8:  Multiple regression for controlling differences in a system (SAS or SPSS)
Project 9:  Classification using Logistic Regression Model (SPSS)

Chapter 1:    5, 6, 7, 16, 19, 20, 21, 23, 27, 28, 29, 32, 33, 38, 40
Chapter 2:    1, 4, 6, 8, 12, 13, 17, 18, 23, 25, 30, 32, 34, 45, 46, 49
Chapter 3:    1, 2, 3, 5, 8, 25, 26
Chapter 4:    4
Chapter 5:    2, 3, 6, 9, 19, 25
Chapter 6:    5, 6, 7, 8, 29
Chapter 7:    3, 12, 16, 24, 56
Chapter 8:    9, 10, 11, 15, 16, 20
Chapter 9:    9, 13, 14, 19, 20, 22
Chapter 10:  15, 16
Chapter 11:  4, 10, 14, 17
Chapter 14:  7, 11