Catalog

Statistics for Applications

Mathematics MIT CC BY-NC-SA 4.0 22 lectures

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. 1 Lecture 1: Introduction to Statistics
  2. 2 Lecture 2: Introduction to Statistics (cont.)
  3. 3 Lecture 3: Parametric Inference
  4. 4 Lecture 4: Parametric Inference (cont.) and Maximum Likelihood Estimation
  5. 5 Lecture 5: Maximum Likelihood Estimation (cont.)
  6. 6 Lecture 6: Maximum Likelihood Estimation (cont.) and the Method of Moments
  7. 7 Lecture 7: Parametric Hypothesis Testing
  8. 8 Lecture 8: Parametric Hypothesis Testing (cont.)
  9. 9 Lecture 9: Parametric Hypothesis Testing (cont.)
  10. 10 Lecture 11: Parametric Hypothesis Testing (cont.) and Testing Goodness of Fit
  11. 11 Lecture 12: Testing Goodness of Fit (cont.)
  12. 12 Lecture 13: Regression
  13. 13 Lecture 14: Regression (cont.)
  14. 14 Lecture 15: Regression (cont.)
  15. 15 Lecture 17: Bayesian Statistics
  16. 16 Lecture 18: Bayesian Statistics (cont.)
  17. 17 Lecture 19: Principal Component Analysis
  18. 18 Lecture 20: Principal Component Analysis (cont.)
  19. 19 Lecture 21: Generalized Linear Models
  20. 20 Lecture 22: Generalized Linear Models (cont.)
  21. 21 Lecture 23: Generalized Linear Models (cont.)
  22. 22 Lecture 24: Generalized Linear Models (cont.)

Course materials