Devlina Chatterjee
Professor
Department of Management Sciences
IIT Kanpur, 208016
devlina@iitk.ac.in
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MBA652
Statistical Modeling for Business Analytics
Course Syllabus
Semester: 2022 Spring
Timings: Th: 12:00 pm – 1:15 pm
Classroom: Online zoom meetings
Instructor: Dr. Devlina Chatterjee, Room 211, IME Building
Ph: 259 6960 (Office)
Email: devlina@iitk.ac.in
Objective of the course:

This is an applied econometrics course. 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 open source statistical softwares – R/Python. 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.
 
 
Text Book:
Introduction to Econometrics by James H. Stock and Mark W. Watson (Addison-Wesley, 3rd Edition)
 
Reference Material:
  • Introductory Econometrics: A Modern Approach, by Jeffrey M. Wooldridge (South-Western Cengage Publishers, 4th Ed.) Ed. Christopher R. Thomas and S. Charles Maurice
  • R resources: http://www.r-tutor.com/r-introduction, http://www.statmethods.net/
Evaluation scheme:
In order to benefit from this course, active participation is required from the students in classes and also out of classes doing assignments and project work.
Attendance* - 10%
Quizzes - 20%
Term Projects (3) - 40%
Final exam - 30%
*(minimum of 65% attendance required - else deregistered from the course).
Attendance mandatory on the days of project presentations)
Academic Integrity:
If you are caught cheating or copying on any assignment, quiz or exam, you will
  1. get ZERO marks on that assignment/quiz/exam.
  2. an additional penalty may be given including and upto assigning a grade F in the course.
Online course management:
  • Course materials and lectures will be available at mooKit
    (https://hello.iitk.ac.in/course/mba652a)
  • Lecture videos (typically 2-3/week) will be uploaded one week early
  • Students need to watch the lecture videos before they come to discussion sessions.
  • There will be one discussion session only per week – on Thursdays.
  • Discussion sessions will be used to clarify doubts but not go over the entire lecture again.
  • Small pop quizzes will be held during discussion sessions to encourage students to go through the lecture videos – these will add to attendance marks.
  • You can also post doubts on online discussion groups / hangout in mooKit
  • In case there are connectivity issues faced by some students, another slot may be used.
  • Main learning will occur through the group projects that students will work on.
  • Tentative dates for project presentation shared below.
Course Schedule
Week Lecture Number Topic
1 Lecture 1 Introduction to the Course
1 Lecture 2 Introduction to Econometrics
2 Lecture 3 Review of Statistical Theory – 1
2 Lecture 4 Review of Statistical Theory – 2
2 Lecture 5 Review of Statistical Theory – 3
3 Lecture 6 Hypothesis Testing
3 Lecture 7 Hypothesis Testing
4 Lecture 8 Linear Regression with One Regressor
4 Lecture 9 Linear Regression with One Regressor
5 Lecture 10 Linear Regression with One Regressor
5 Lecture 11 Linear Regression with One Regressor – Hypothesis tests
5 Lecture 12 Regression with One Regressor - Dummy Variables, Heteroskedasticity
6 Lecture 13 Linear Regression with Multiple Regressors
6 Lecture 14 Linear Regression with Multiple Regressors
6 Lecture 15 Linear Regression with Multiple Regressors
Mid-sem exams
7 Lecture 16 Linear Regression with Multiple Regressors – Hypothesis tests
7 Lecture 17 Joint Hypothesis Tests – F tests
8 Lecture 18 Control Variables – Multiple regression
8 Lecture 19 Panel Data Regression
9 Lecture 20 Panel Data Regression
9 Lecture 21 Panel Data Regression
10 Lecture 22 Regression with Binary Dependent Variables
10 Lecture 23 Regression with Binary Dependent Variables
11 Lecture 24 Regression with Binary Dependent Variables – Estimation, Hypothesis Testing
11 Lecture 25 Regression with Binary Dependent Variables - Goodness of fit
12 Lecture 26 Regression with Binary Dependent Variables - Example
12 Lecture 27 Non-linear regression - Polynomial Functions
13 Lecture 28 Non-linear regression – Logarithmic Functions
13 Lecture 29 Non-linear regression - Interaction variables
14 3rd Project presentations
Project presentations:
Group project Topic Dates
1 Multiple Linear Regression TBD
2 Panel Data Regression TBD
3 Binary dependent variables regression TBD