APM 630 REGRESSION ANALYSIS



APM 630 REGRESSION ANALYSIS

Instructor: Dr. Lianjun Zhang

Office : Room 323 Bray Hall

Phone : (315) 470-6558

e-mail : lizhang@esf.edu

Class : M.W.F. 1:50 - 2:45 pm, Bray Hall Room 315

Office Hour: M.W.F. 3:00 - 4:00 pm or by appointment

Textbook : (1) Rawlings, Pantula, and Dickey. 1998. Applied Regression Analysis: A

Research Tool. 2nd Ed. Springer.

(2) SAS 9.0, 9.1, and 9.2 Online Docs (both HTML and PDF) at



Course Objectives:

APM 630 is a course in APPLIED Regression Analysis. This course is designed to teach students how to develop simple linear, multiple linear, and nonlinear regression models using Statistical Analysis System (SAS). The primary emphasis is on the methods and applications of regression techniques. Lectures cover necessary theory, criteria and strategies for selecting "best" models, and detecting flaws of the models. Examples in forestry, biology and social sciences will be used to illustrate the applications of the techniques. SAS programs for each example are provided, and the interpretation of results (computer outputs) is emphasized.

Course Outline:

1. Introduction and Basic Statistics: basic statistics, t-test, F-test, ANOVA, correlation analysis

2. Simple Linear Regression: least squares estimation, assumptions, hypothesis testing, prediction

3. Matrix Algebra

4. Multiple Linear Regression: estimation, hypothesis testing, stepwise, model selection, case study

5. Project 1

6. Indicator or Dummy Variables in Regression

7. Residual Analysis

8. Transformation and Weighted Least Squares

9. Project 2

10. Influence Diagnostics

11. Multicollinearity

12. Nonlinear Regression Models

13. Linear Mixed Models

14. Project 3

References:

Myers, R.H. 1990. Classical and Modern Regression with Applications. 2nd Ed. PWS-Kent.

Montgomery, D. C., and E. A. Peck. 1992. Introduction to Linear Regression Analysis. 2nd Ed. John Wiley & Sons.

Neter, J., M.H. Kuter, C.J. Nachtsheim, and W. Wasserman. 1996. Applied Linear Regression Models. 3rd Ed. IRWIN.

Evaluation:

Your progress will be evaluated by the following weights:

Assignments (300 points) 25%

Project 1 (100 points) 25%

Project 2 (100 points) 25%

Project 3 (100 points) 25%

Note:

(1) Projects will usually require statistical analysis, model development, and interpretation of the results. You may work with other students on statistical computing and discussion of potential solutions. You will be expected to submit your own report for the analysis results. Copying the report from each other is NOT acceptable.

(2) Your project reports should include (1) introduction, purpose, and description of the project and data sets, (2) step-by-step analysis methods, (3) models, analysis results and discussion, including necessary tables and graphics, and (4) summary. Writing your report on the SAS output is NOT acceptable. Attach your SAS programs and outputs as the appendix.

Grading System:

Your final grade will be determined as follows:

95 - 99 = A

90 - 94 = A-

85 - 89 = B+

80 - 84 = B

75 - 79 = B-

< 75 = F

Good Luck!

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