Share Description
In this course, you will :
- Explore some of the many real-world applications of random variables.
- What is the definition of a discrete random variable?
- Learn how to assign probabilities to random variables.
- Investigate the most important aspect of the expected value.
- Learn how to calculate the influence of two variables on each other.
- Learn what it means when a distribution has "no memory."
- Learn about a type of continuous distribution that you've probably noticed popping up everywhere!
- Investigate a distribution method used by insurance companies all over the world.
- Learn why there is a similar distribution for radioactivity and winning the lottery.
- Learn how to model stock market fluctuations in a straightforward manner.
Syllabus :
1. Introduction
- Random Variables
- Distributions
- Random Variable Applications
2. Discrete Random Variables
- Definition
- Density Functions
- Joint Distributions
3. Expected Value
- Expected Value Definition and Properties
- Expected Value Calculations
- Conditional Expectation
- Linearity of Expectation
4. Variance
- Variance Definition and Properties
- Variance and Standard Deviation
- Covariance
5. Discrete Distributions
- Uniform Discrete Distribution
- Bernoulli Distribution
- Binomial Distribution
- Geometric Distribution
6. Continuous Random Variables
- Definition
- Density Functions
- Joint Distributions
- Expected Value and Variance
7. Continuous Distributions
- Normal Distribution
- Exponential Distribution
- Gamma Distribution
- Log-Normal Distribution
