My Courses

I usually teach two and one/third courses in a given academic year, one and one/third in the August term and one in the January term. The courses I teach in the August term are MG221 (full) and MG301 (one/third), and in January I teach MG226. Brief description of the courses are as follows.


MG 221: Applied Statistics 2:1
It is offered in the August term and will cover topics like Introduction to Sampling Distributions, Estimation and Hypothesis testing through One and Two sample problem for mean, variance and proportions. Basics of Design of Experiments and Factorial Analysis of Variance Models. Simple and Multiple Correlation and Regression Analysis. Chi-square analysis for categorical data. (Time permitting) Logistic Regression Analysis.

Suggested Texts:
  1. Jonhn Neter, Willam Wasserman & Michale H. Kutner: Applied Linear Statistical Models. Irwin. Third Edition.
  2. David W. Hosmer & Stanley Lemeshow: Applied Logistic Regression. Wiley. Second Edition.
  3. C. R. Rao: Linear Statistical Inference and its Applications. Wiley. Second Edition.
Prerequisite:
Some knowledge of Probability Theory and Probability Distributions, particularly Binomial and Normal.

Who should take it?
It is a compulsory course for all students (M.Mgt, M.Sc.(Engineering) & Ph.D.) starting their degree programme in the Department of Management Studies at the Indian Institute of Science.


MG 301: Methodology of Management Research 3:0
MG 301 is taught by two faculty members in the August term and I pitch in towards the end of the course in late October/early November. I essentially cover Applied Multivariate Analysis in my part of the course. The topics covered include Introduction to Multivariate Normal Distribution, Sampling Distribution of Sample Mean Vector and Covariance Matrix, One and Two Sample Problem, Multivariate Analysis of Variance, Discriminant Analysis, Principal Component Analysis, Cluster Analysis and Factor Analysis.

Suggested Texts:
  1. Richard A. Johnson & Dean W. Wichern: Applied Multivariate Statistical Analysis. Prentice Hall. Third Edition.
  2. T. W. Anderson: Introduction to Multivariate Statistical Analysis. Wiley. Second Edition.
  3. C. R. Rao: Linear Statistical Inference and its Applications. Wiley. Second Edition.
Prerequisite:
Applied Statistics or equivalent, basically knowledge of basic Applied Statistics focusing on Analysis of Variance and Regression Analysis is required for this course.

Who should take it?
MG 301 is a compulsory course for the research students (both M.Sc. (Engineering) and Ph.D.) in the Department of Management Studies at the Indian Institute of Science. It is not available to the first year M.Mgt students as all the courses in the first semester (August term) are compulsory for them with no room for electives. However the second year M.Mgt students can take it as an elective in their third semester. It will be useful for those M.Mgt students specializing in Business Analytics.


MG 226: Time Series Analysis and Forecasting 3:0
This course is offered in the January term which is a stream core for those M.Mgt students specializing in Business Analytics. It starts with traditional Time Series Analysis and then mainly focuses on ARIMA modeling and forecasting using such models. Issues involving Frequency Domain Analysis, Seasonal ARIMA, VAR, ECM-Conitegration and ARCH-GARCH modeling are also touched upon in the course.

Suggested Texts:
  1. Brockwell, Peter J & Davis, Richard A: Time series: Theory and methods. Springer series in Statistics. Second Edition.
  2. Chatfield, Chris: Analysis of Time Series: an Introduction. Chapman & Hall. Sixth Edition.
  3. Lutkepohl, Helmut: Introduction to Multiple Time Series Analysis. Springer-Verlag.
Prerequisite:
Managerial Statistics or equivalent. Knowledge of Probability Theory and Regression Analysis is required for this course.

Who should take it?
It is a compulsory stream core for those M.Mgt students specializing in Business Analytics. Most of the examples are drawn from the domain of Financial and Econometric applications. Research students interested in working in Empirical Finance or Econometric Applications will also find the course useful.