Lecture Slides2.1: Elements of Probability.

Lecture Slides 2.2: Conditional Probability.

Lecture Slides 4.1: Distribution functions. Expectation.

Lecture Slides 4.2: Expectation of a function. Variance.

Lecture Slides 5.1: Binomial Distribution.

Lecture Slides 5.2: Poisson, geometric, negative geometric distributions.

Lecture Slides 6.1: Continuous random variables: density function and it's properties.

Lecture Slides 6.2: Expected value, Variance, Cumulative distribution.

Lecture Slides 7: Normal Distribution: Definition, Calculating Probabilities using Standard Normal Distribution.

Lecture Slides 9: Limit theorems: the Law of Large Numbers; Central Limit Theorem.

Lecture Slides 12.1: Statistical estimation. Maximum likelihood estimates.

Lecture Slides 12.2: Confidence intervals for population mean.

Lecture Slides 13.1: Confidence intervals for population proportions, variance.

Lecture Slides 13.2: Hypothesis test for population mean.

Lecture Slides 14: Hypothesis test for proportions, variance

Lecture Notes 1.1: Introduction

Lecture Notes 1.2: Combinatorial Analysis

Lecture Notes 2.1: Elements of Probability.

Lecture Notes 2.2: Conditional Probability.

Lecture Notes 3.1: Independent Events.

Lecture Notes 3.2: Random Variables.

Lecture Notes 4.1: Distribution functions. Expectation.

Lecture Notes 4.2: Expectation of a function. Variance.

Lecture Notes 5.1: Binomial distribution and it's properties.

Lecture Notes 5.2: Poisson, Negative Binomial & Geometric distributions.

Lecture Notes 6.1: Continuous random variables: density function and it's properties.

Lecture Notes 6.2: Expected value & Variance.

Lecture Notes 7.2: Standard normal distribution.

Lecture Notes 9: Limit theorems: the Law of Large numbers; Central Limit Theorem.

Lecture Notes 10.1: Joint distributions. Covariance.

Lecture Notes 10.2: Distribution of sum of two variables; Expected value of sum of two variables.