- Lecture 40 Convergence in Distribution
- Lecture 38-39 Convergence Concepts
- Lecture 36-37 Order Statistics
- Lecture 35 The Derived Distributions Student’s t and Snedecor’s F
- Lecture 34 Properties of the sample mean and variance
- Lecture 33 Sums of Random Variables from a Random Sample
- Lecture 31-32 Basic Concepts of Random Samples
- Lecture 28-30 Multivariate Distribution
- Lecture 27 Covariance and Correlation
- Lecture 25-26 Hierarchical Models and Mixture Distributions
- Lecture 24 Bivariate Transformations
- Lecture 23 Conditional Distributions and Independence
- Lecture 22 Multiple Random Variables
- Lecture 21 Inequalities and Identities
- Lecture 20 Exponential Families
- Lecture 19 Beta Distribution
- Lecture 18 Normal Distribution
- Lecture 16-17 Continuous Distribution
- Lecture 14-15 Negative Binomial Distribution
- Lecture 13 Poisson Distribution
- Lecture 12 Binomial Distribution
- Lecture 11 Moment Generating Function
- Lecture 7-10 Transformations and Expectations
- Lecture 6 Density and Mass Functions
- Lecture 5 Random Variable
- Lecture 4 Conditional Probability and Independence
- Lecture 3 Counting and Equally Likely Outcomes
- Lecture 2 Basics of Probability
- Lecture 1 Set Theory
- Probability distribution
- Variables, probability distributions, marginal and joint distributions and density functions
- Concept of random variables, discrete and continuous one and two dimensional random
- Conditional probability, independent events, Baye’s formula.
- Basic concept of probability
- Significant digits
- Descriptive & Inferential Statistics