NOC:Introduction to Probability Theory and Statistics

Lecture 1 - Random experiment, sample space, axioms of probability, probability space

Lecture 2 - Random experiment, sample space, axioms of probability, probability space (Continued...)

Lecture 3 - Random experiment, sample space, axioms of probability, probability space (Continued...)

Lecture 4 - Conditional probability, independence of events

Lecture 5 - Multiplication rule, total probability rule, Bayes's theorem

Lecture 6 - Definition of Random Variable, Cumulative Distribution Function

Lecture 7 - Definition of Random Variable, Cumulative Distribution Function (Continued...)

Lecture 8 - Definition of Random Variable, Cumulative Distribution Function (Continued...)

Lecture 9 - Type of Random Variables, Probability Mass Function, Probability Density Function

Lecture 10 - Type of Random Variables, Probability Mass Function, Probability Density Function (Continued...)

Lecture 11 - Distribution of Function of Random Variables

Lecture 12 - Mean and Variance

Lecture 13 - Mean and Variance (Continued...)

Lecture 14 - Higher Order Moments and Moments Inequalities

Lecture 15 - Higher Order Moments and Moments Inequalities (Continued...)

Lecture 16 - Generating Functions

Lecture 17 - Generating Functions (Continued...)

Lecture 18 - Common Discrete Distributions

Lecture 19 - Common Discrete Distributions (Continued...)

Lecture 20 - Common Continuous Distributions

Lecture 21 - Common Continuous Distributions (Continued...)

Lecture 22 - Applications of Random Variable

Lecture 23 - Applications of Random Variable (Continued...)

Lecture 24 - Random vector and joint distribution

Lecture 25 - Joint probability mass function

Lecture 26 - Joint probability density function

Lecture 27 - Independent random variables

Lecture 28 - Independent random variables (Continued...)

Lecture 29 - Functions of several random variables

Lecture 30 - Functions of several random variables (Continued...)

Lecture 31 - Some important results

Lecture 32 - Order statistics

Lecture 33 - Conditional distributions

Lecture 34 - Random sum

Lecture 35 - Moments and Covariance

Lecture 36 - Variance Covariance matrix

Lecture 37 - Multivariate Normal distribution

Lecture 38 - Probability generating function and Moment generating function

Lecture 39 - Correlation coefficient

Lecture 40 - Conditional Expectation

Lecture 41 - Conditional Expectation (Continued...)

Lecture 42 - Mode of Convergence

Lecture 43 - Mode of Convergence (Continued...)

Lecture 44 - Law of Large Numbers

Lecture 45 - Central Limit Theorem

Lecture 46 - Central Limit Theorem (Continued...)

Lecture 47 - Descriptive Statistics and Sampling Distributions

Lecture 48 - Descriptive Statistics and Sampling Distributions (Continued...)

Lecture 49 - Descriptive Statistics and Sampling Distributions (Continued...)

Lecture 50 - Point estimation

Lecture 51 - Methods of Point estimation

Lecture 52 - Interval Estimation

Lecture 53 - Testing of Statistical Hypothesis

Lecture 54 - Nonparametric Statistical Tests

Lecture 55 - Analysis of Variance

Lecture 56 - Correlation

Lecture 57 - Regression

Lecture 58 - Logistic Regression