The Normal Distribution: Crash Course Statistics #19

A quick explainer for Statistics for Applications (MIT 18.650) by CrashCourse. 11 min. Watch on YouTube

Course: Statistics for Applications (MIT 18.650) (Mathematics). Covers: statistical inference estimation hypothesis testing regression bayesian.

All lectures in Statistics for Applications (MIT 18.650)

  1. 1. Introduction to Statistics · 1 h 18 min
  2. 2. Introduction to Statistics (cont.) · 1 h 17 min
  3. 3. Parametric Inference · 1 h 23 min
  4. 4. Parametric Inference (cont.) and Maximum Likelihood Estimation · 1 h 18 min
  5. 5. Maximum Likelihood Estimation (cont.) · 1 h 17 min
  6. 6. Maximum Likelihood Estimation (cont.) and the Method of Moments · 1 h 19 min
  7. 7. Parametric Hypothesis Testing · 1 h 19 min
  8. 8. Parametric Hypothesis Testing (cont.) · 1 h 19 min
  9. 9. Parametric Hypothesis Testing (cont.) · 1 h 21 min
  10. 11. Parametric Hypothesis Testing (cont.) and Testing Goodness of Fit · 1 h 23 min
  11. 12. Testing Goodness of Fit (cont.) · 1 h 21 min
  12. 13. Regression · 1 h 16 min
  13. 14. Regression (cont.) · 1 h 14 min
  14. 15. Regression (cont.) · 1 h 15 min
  15. 17. Bayesian Statistics · 1 h 18 min
  16. 18. Bayesian Statistics (cont.) · 1 h 3 min
  17. 19. Principal Component Analysis · 1 h 17 min
  18. 20. Principal Component Analysis (cont.) · 1 h 17 min
  19. 21. Generalized Linear Models · 1 h 15 min
  20. 22. Generalized Linear Models (cont.) · 1 h 17 min
  21. 23. Generalized Linear Models (cont.) · 1 h 18 min
  22. 24. Generalized Linear Models (cont.) · 55 min