MIT 18.650 Statistics for Applications, Fall 2016 (English)

   Thursday, December 12, 2024   

Starting a new Lecture Notes Series on MIT 18.650 Statistics for Applications, Fall 2016

Youtube Lecture Playlist Credits 

Channel Name: MIT OpenCourseWare

So Let Us Start to This Journey of Learning 

MIT 18.650 Statistics for Applications, Fall 2016 By Lecture Notes together!

Lecture 1: 1. Introduction to Statistics

Lecture 2: 2. Introduction to Statistics (cont.)

Lecture 3: 3. Parametric Inference

Lecture 4: 4. Parametric Inference (cont.) and Maximum Likelihood Estimation

Lecture 5: 5. Maximum Likelihood Estimation (cont.)

Lecture 6: 6. Maximum Likelihood Estimation (cont.) and the Method of Moments

Lecture 7: 7. Parametric Hypothesis Testing

Lecture 8: 8. Parametric Hypothesis Testing (cont.)

Lecture 9: 9. Parametric Hypothesis Testing (cont.)

Lecture 10: 11. Parametric Hypothesis Testing (cont.) and Testing Goodness of Fit

Lecture 11: 12. Testing Goodness of Fit (cont.)

Lecture 12: 13. Regression

Lecture 13: 14. Regression (cont.)

Lecture 14: 15. Regression (cont.)

Lecture 15: 17. Bayesian Statistics

Lecture 16: 18. Bayesian Statistics (cont.)

Lecture 17: 19. Principal Component Analysis

Lecture 18: 20. Principal Component Analysis (cont.)

Lecture 19: 21. Generalized Linear Models

Lecture 20: 22. Generalized Linear Models (cont.)

Lecture 21: 23. Generalized Linear Models (cont.)

Lecture 22: 24. Generalized Linear Models (cont.)