Devlina Chatterjee
Associate Professor
Industrial and Management Engineering
IIT Kanpur, 208016


Statistical Modeling for Business Analytics
Course Syllabus
Semester: 2018 Spring
Timings: Mon, Wed   5:10 – 6:25 pm
Classroom: New IME Building – Room C5
Instructor: Dr. Devlina Chatterjee, Room 211, IME Building
Ph: 259 6960 (Office)
Email: (Please always write MBA652-2018 in the subject line)
Objective of the course:
This is an applied econometrics course. It is expected that students taking this course will gain skills and experience in data analysis, economic modeling and interpretation of results. The course will include hands-on model building using the open source statistical software - R. Emphasis will be laid on the ability to set up the model correctly and interpretation the results of the models. Students should also develop the ability to critically evaluate the quality of statistical models used in research studies.
Introduction to Economic Questions and Data, Review of Probability, Review of Statistics, Linear regression with one regressor, Regression with multiple regressors, Non-linear regression functions, Assessing studies based on linear regression (internal and external validity), Regression with a binary dependent variable, Panel Data Regression, Introduction to Time-series regression and forecasting, Estimation of Dynamic Causal Effects,VAR, ARCH and GARCH models.
Text Book:
Introduction to Econometrics by James H. Stock and Mark W. Watson (Addison-Wesley, 3rd Edition)
Reference Books:
  • Introductory Econometrics: A Modern Approach, by Jeffrey M. Wooldridge (South-Western Cengage Publishers, 4th Ed.)

  • Basic Econometrics by D. Gujarati (McGraw-Hill, 5th Edition)
Evaluation scheme:
Attendance* - 10%
Quizzes(2 or 3) - 10%
Mid-Term Exam - 20%
Term Project(2) - 30%
End-Term Exam - 30%

* (minimum of 65% attendance required - else deregistered from the course).
Attendance mandatory on the days of project presentations)

Topics to be covered:
1. Introduction - Economic questions and data (Ch1, SW)
2. Review of probability (Ch 2, SW)
3. Review of statistics (Ch 3, SW)
4. Linear regression with one regressor (Ch4, SW)
5. Regression with a single regressor: hypothesis tests and confidence intervals (Ch5, SW)
6. Linear regression with multiple regressors (Ch6, SW)
7. Hypothesis tests and confidence intervals in multiple regression (Ch7, SW)
8. Nonlinear regression functions
Mid-sem exam
(Ch8, SW)
9. Regression with binary dependent variable (Ch11, SW)
10. Regression with panel data (Ch10, SW)
11. Introduction to time series regression (Ch14, SW)
12. Estimation of dynamic causal effects
End-sem exam
(Ch15, SW)