Video reviews the notation of the expected value and variance of random variables. It then explains the expected value and variance of the sum of two random variables and explains why the variance of the difference of random variables is...

Defining discrete and continuous random variables. Working through examples of both discrete and continuous random variables.

A video abour probability density functions for continuous random variables.

Basic idea and definitions of random variables. [5:32]

What is the best way to distribute our points? You can explore the interactive program used in this video [4:21].

Sal Khan introduces the magic behind the law of large numbers. [8:59]

Sal Khan breaks down how to create the probability distribution of the number of "heads" after 3 flips of a fair coin. [6:47]

Linear transformations (addition and multiplication of a constant) and their impacts on center (mean) and spread (standard deviation) of a distribution.

Example of transforming a discrete random variable.

Using a TI-84 (very similar for TI-85 or TI-89) calculator for making calculations regarding geometric random variables.

Finding the probability for a single outcome of a geometric random variable.

Distinguishing between geometric and binomial random variables.

Finding the variance and standard deviation of a discrete random variable.

Basic idea and definitions of random variables.

Finding the mean (or expected value) of a discrete random variable.

Example analyzing discrete probability distribution.

Sal breaks down how to create the probability distribution of the number of 'heads' after 3 flips of a fair coin.

Defining discrete and continuous random variables. Working through examples of both discrete and continuous random variables.

Probability density functions for continuous random variables.

Examples finding probabilities from probability distributions for continuous random variables.

Finding the probability that the total of some random variables exceeds an amount by understanding the distribution of the sum of normally distributed variables.

Example showing how to find the mean and standard deviation of a binomial random variable.