Instructor:           Dr. Guang-Hwa Andy Chang
Office:                 1032 Cushwa Hall
Phone Number:   (330) 742-1818
E-mail:                 chang@math.ysu.edu
Office hours:       12:00 - 13:00, M T W & F, and 17:10 – 17:40 , M & W, or by appointment.
Text:  "Applied Linear Statistical Models", by Neter, Kutner, Nachtsheim and Wasserman, 4th edition, Irwin, 1996.
Statistical Software:   SAS and SPSS 8.0, 9.0, 10.0

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
*	Instructor's Notes (If time permit)

Grading Policy:

     Two tests --------------------------------- 30%
     Homework & projects -------------------  40%
     Final Exam -------------------------------  30%
     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.
• All homework and projects have to be turned in on time.  Late submissions will be penalized.
• 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 2, 1999.

• Projects msut be done by a wordprocessor, printed on 8.5 x 11 paper and stapled (if more than one page).
• Put your name and date on the top left of the first page.
• In some of the projects, you will be asked to find a data set that you find interesting as a basis of the analysis.  The data can be in any subject area, such as sports, business, engineering, public health, education, etc.  In such case, your data set must contain at least 50 cases.  Obtain your data from an appropriate source and describe where or how did you obtian it in your paper.  Do not just take a data set from a textbook.
• Technical results should be stated clearly.  Computer output that is relevant to the results can be put in the appendix section with proper title or label.  However, you must point out that they constitute a solution to the problem in your writing with a proper reference.  Do not include any unnecessary output in your paper.
• A good project should show your understanding of the problem and possible solutions or techniques to consider.
• Each project must contain a conclusion section giving a non-technical summary of the analysis.  You must show your understanding of the problem in the paper.  Preparing your paper as if you are a consultant or you are writing a professional journal article.  You are presenting some technical details of the analysis and with a summary that any person can also understand the results of your study without the details.

Homework & Projects:

Project 1:   Simple Linear Regression. (Due 11/5)
Project 2:   Salary Adjustment. see handout (Due 11/23)
Final Project:   Study of Risk Factors (Due 12/10)

SPSS Assignment 1:    Descriptive Statistics. (Due 10/1)
SPSS Assignment 2:    Simple Linear Regression and Prediction. (Due 10/15)
SPSS Assignment 3:    Find a good model.
SPSS Assignment 4:    Lack of Fit?  (Due 11/3)
SPSS Assignment 5:    Multiple Regression Model and Multicollinearity. see handout (Due 11/15)
SPSS Assignment 6:    Interaction Effects and Indicator Variable. see class note (Due 11/19)
SPSS Assignment 7:    Classification Problem.  (Due 12/3)

Play the simple regression games: Regression Line, Correlation

Homework:
Chapter 1:    5, 6, 7, 16, 19, 20, 21, 23, 25, 27, 28, 29, 30, 32, 33, 34, 35, 36, 38, 40
Chapter 2:    1, 4, 6, 8, 12, 13, 15, 17, 18, 19, 23, 25, 30, 32, 34, 37, 38, 39, 43, 45, 46, 49
Chapter 3:    1, 2, 3, 5, 8, 9, 17, 25, 26
Chapter 4:    4, 8, 9, 12, 16, 19
Chapter 5:    2, 3, 4, 6, 9, 12, 15, 17, 19, 21, 23, 25
Chapter 6:    5, 6, 7, 8, 18(a-e), 29, 31
Chapter 7:    3, 7, 8, 12, 15,16, 24, 31, 39, 56
Chapter 8:    9, 10, 11, 15, 16, 20
Chapter 9:    9, 12, 13, 14, 18, 19, 20, 22, 23
Chapter 10:  15, 16
Chapter 11:  4, 9, 10, 14, 17
Chapter 14:  7, 11