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Maximum likelihood is a popular method of estimating population parameters from a sample. It is an important component in most modeling methods, and maximum likelihood estimates are used as benchmarks against which other methods are often measured.

This online course, will cover the derivation of maximum likelihood estimates, and their properties.

After successfully completing this course, you will understand the role that MLE plays in statistical models, and be able to assess both the advantages and disadvantages of using a maximum likelihood estimate in a particular situation. This course will provide useful conceptual foundation for those contemplating taking statistical modeling courses.

Course Program:

  • Week 1: Basics of Estimation, What is a ML Estimator?
  • Week 2: Properties and Applications of ML Estimators and Bonus Readings

Important Date:

November 28, 2014 to December 12, 2014


2 Weeks

Time Requirement:

About15 hours per week, at time of your choosing.


INR 17,940 (assuming $ = INR 60)

Part Time/Full Time:

Part Time

Maximum likelihood estimation is used in many of the methods taught in’s intermediate and advanced courses (Survival Analysis, Logistic Regression and Generalized Linear Models, to name a few). Students who need to understand the theory behind those methods should take this course first.

  • SAS
  • R
  • S-plus
  • Stata
  • Minitab
  • StatXact
  • KuberDeokar
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