Statistics for Applications

Statistics for Applications

Prof. Philippe Rigollet via MIT OpenCourseWare Direct link

1. Introduction to Statistics

1 of 22

1 of 22

1. Introduction to Statistics

Class Central Classrooms beta

YouTube videos curated by Class Central.

Classroom Contents

Statistics for Applications

Automatically move to the next video in the Classroom when playback concludes

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

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.