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Essentials of Probability and Statistical Inference III: Theory of Modern Statistical Methods

East Baltimore
3rd term
4 credits
Academic Year:
2022 - 2023
Instruction Method:
Hybrid In-person and Synchronous Online
Class Times:
  • M W,  10:30 - 11:50am
Auditors Allowed:
Undergrads Allowed:
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructor:
Ni Zhao

Working knowledge of calculus


Builds on the concepts discussed in 140.646 and 140.647 to lay out the foundation for both classical and modern theory/methods for drawing statistical inference. Includes classical unbiased estimation, unbiased estimating equations, likelihood and conditional likelihood inference, linear models and generalized linear models, and other extended topics. De-emphasizes mathematical proofs and replaces them with extended discussion of interpretation of results and examples for illustration.

Learning Objectives:

Upon successfully completing this course, students will be able to:

  1. Describe the theoretical basis for the current methods used in statistical analysis.
  2. Describe the theoretical basis for the current methods used in statistical analysis.
Methods of Assessment:

This course is evaluated as follows:

  • 50% 4-5 problem sets
  • 50% Final Exam

Instructor Consent:

Consent required for some students

Consent Note:

Consent required only for students who have not taken 140.646 and 140.647

For consent, contact:

Special Comments:

Please note: This is the virtual/online section of a course that is also offered onsite. Students will need to commit to the modality for which they register. One 1-hour lab per week (time TBA)