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  1. Lecture 40 Convergence in Distribution
  2. Lecture 38-39 Convergence Concepts
  3. Lecture 36-37 Order Statistics
  4. Lecture 35 The Derived Distributions Studentís t and Snedecorís F
  5. Lecture 34 Properties of the sample mean and variance
  6. Lecture 33 Sums of Random Variables from a Random Sample
  7. Lecture 31-32 Basic Concepts of Random Samples
  8. Lecture 28-30 Multivariate Distribution
  9. Lecture 27 Covariance and Correlation
  10. Lecture 25-26 Hierarchical Models and Mixture Distributions
  11. Lecture 24 Bivariate Transformations
  12. Lecture 23 Conditional Distributions and Independence
  13. Lecture 22 Multiple Random Variables
  14. Lecture 21 Inequalities and Identities
  15. Lecture 20 Exponential Families
  16. Lecture 19 Beta Distribution
  17. Lecture 18 Normal Distribution
  18. Lecture 16-17 Continuous Distribution
  19. Lecture 14-15 Negative Binomial Distribution
  20. Lecture 13 Poisson Distribution
  21. Lecture 12 Binomial Distribution
  22. Lecture 11 Moment Generating Function
  23. Lecture 7-10 Transformations and Expectations
  24. Lecture 6 Density and Mass Functions
  25. Lecture 5 Random Variable
  26. Lecture 4 Conditional Probability and Independence
  27. Lecture 3 Counting and Equally Likely Outcomes
  28. Lecture 2 Basics of Probability
  29. Lecture 1 Set Theory
  30. Probability distribution
  31. Variables, probability distributions, marginal and joint distributions and density functions
  32. Concept of random variables, discrete and continuous one and two dimensional random
  33. Conditional probability, independent events, Bayeís formula.
  34. Basic concept of probability
  35. Significant digits
  36. Descriptive & Inferential Statistics