Statistics, Operations Research and Actuarial Science Department  
STERN SCHOOL OF BUSINESS 
NEW YORK UNIVERSITY  
NYU Stern Syllabi Homepage
Course: C22.0003.001- Regression and Forecasting
Semester: Fall 2000
Class Hours: W 3:30 - 4:45 PM
Class Room: Tisch UC-62
Instructor: Prof. Samprit Chatterjee
Office Hours: MR 1:30 - 4:00 pm; W 1:00 - 3:00 pm
Office: 8-50 KMEC Tel: (212) 998-0480
Email: schatter@stern.nyu.edu

 

Syllabus

This course will focus on linear regression analysis. After this semester you should be able to understand the need for probabilistic linear models and should have a working knowledge of simple/multiple linear regression analysis. The only mathematics you will be required to know is basic algebra. The software used in the course will be MINITAB.

There will be homework assigned each week. All assigned problems must be completed by all students, but only specified problems will have to be handed in. Homework must be handed in at the beginning of class. Assigned problems which are not required to be handed in will be reviewed during class and/or solutions will be handed out. Besides HOMEWORK, there will be a MIDTERM and FINAL. There will be a final project. Your grades will be calculated as follows:

Midterm 30%
Homework 10%
Final 30%
Project 30%

Attendance in class is essential. There will be no make-up exams.


Required Text:

Chatterjee, Hadi, Price, Regression Analysis by Example (RABE), 3rd Edition, John Wiley, 2000.


Part I. Simple Linear Regression

  1.  Determining Linear Bivariate Relationships
  2. Least Squares – Methodology and Estimation
  3. Goodness of Fit
  4. Least Squares – Inferences and Prediction

 

Part II. Multiple Linear Regression

  1. The General Linear Model
  2. Least Squares for Multiple Regression – Methodology and Estimation
  3. Goodness of Fit and ANOVA Tables
  4. Least Squares for Multiple Regression – Inferences and Prediction
  5. Regression Diagnostics
  6. Qualitative Variables
  7. Transformation of Variables
  8. Regression with Time Series Data
  9. Multicollinearity
  10. Model Selection
  11. Logistic Regression (Time permitting)

 

These topics are found in RABE Chapters 1-6, 8, 9 , 11. 

 
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