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Massachusetts Institute of Technology

Statistics for Applications (Fall 2016)

Massachusetts Institute of Technology via MIT OpenCourseWare

Overview

Course Features
  • Video lectures
  • Lecture notes
  • Assignments: problem sets (no solutions)
Course Description

This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathematics in the research and development of efficient statistical methods.

Syllabus

1. Introduction to Statistics.
2. Introduction to Statistics (cont.).
3. Parametric Inference.
4. Parametric Inference (cont.) and Maximum Likelihood Estimation.
5. Maximum Likelihood Estimation (cont.).
6. Maximum Likelihood Estimation (cont.) and the Method of Moments.
7. Parametric Hypothesis Testing.
8. Parametric Hypothesis Testing (cont.).
9. Parametric Hypothesis Testing (cont.).
11. Parametric Hypothesis Testing (cont.) and Testing Goodness of Fit.
12. Testing Goodness of Fit (cont.).
13. Regression.
14. Regression (cont.).
15. Regression (cont.).
17. Bayesian Statistics.
18. Bayesian Statistics (cont.).
19. Principal Component Analysis.
20. Principal Component Analysis (cont.).
21. Generalized Linear Models.
22. Generalized Linear Models (cont.).
23. Generalized Linear Models (cont.).
24. Generalized Linear Models (cont.).

Taught by

Prof. Philippe Rigollet

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