1. Course Description and Objectives

GSBA 604, the second in a three course sequence in empirical research methods, covers one of the most useful statistical methods – regression analysis. The fundamental concepts and modeling skills of regression analysis are developed in this course.

  We will cover simple, multiple and nonlinear regression models with focuses on the model development stages and the model building skills. Students will learn how to:

1. specify a tentative model for application
2. estimate this tentative model from a dataset
3. perform extensive diagnostic checks to approximate the dataset
4. modify the model to approximate the dataset more closely
5. validate candidate models with new datasets
6. select a final model and perform estimates
7. use the final model for analysis, prediction and generalization

Applications with students’ own datasets will be used to illustrate the theories, fundamental concepts and skills. Students will obtain hands on experience in developing regression model and using statistical software.

2. Materials

a.       Cook, R. Dennis and Sanford Weisberg 1999 Applied Regression Including Computing and Graphics  John Wiley & Sons, Inc. (CW)

b.      Griffiths, William E., R. Carter Hill and George G. Judge 1993 Learning and Practicing Econometrics, John Wiley & Sons, Inc. (GHJ)

                  click here for more readings.

3. Course Requirements

Each student will need to complete 7 problem sets, one midterm and a final research paper.

Each student will be asked to bring his or her own research question, to select a dataset, and to build a regression model by following the model building stages and using the skills learned from this course.

4. Problem Sets

The emphasis of the problem sets is on understanding statistical concepts and obtaining familiarities with computing methods. Late homework will NOT be accepted. Your discussion may include salient computer output (cut and pasted).  Do not include unnecessary computer output in your problem sets.

Due dates for problem sets are indicated on each assignment.  Late homework will NOT be accepted.  If you have not completed an assignment by the due date, turn in what you have to receive partial credit.

5. Final Research Paper

Students are expected to bring one research question and a dataset to this course. Then, each student will apply the learned model building skills to his or her own research, and go through the model development stages to obtain a final model. A research paper based on this final model and the analyses need to be completed.  

There are five key components in the research paper:

• A critical review of the appropriate academic literature establishing the current state of debate, opinions on the issues, findings or short-comings.
• A clear and concise research question establishing the focus of the study and, if applicable how it differ from previous studies.
• The operationalization and measurement of the principal issues in the research question.
• The application of the appropriate statistical procedure and an interpretation of the results.
• A discussion of the implications and conclusions and how it pertains to the issue or debate.

6. Grades

The course grade will be determined using the following distribution:

                                                            Percentage

                                    Problems Sets                                    35%

                                    Midterm                                 25%

                                    Final Paper                           40%

7. Software

We will use JMPIN. But students are encouraged to use S+ or R.

8. Lectures