Learn about probability distribution models, including normal distribution, and continuous random variables to prepare for a career in information and data science.
This is part 2 of 2. Part 1 is available here: https://www.youtube.com/playlist?list=PLNAxrEh0NNf428tm3dhwcbVdviEWpoj6S
Part 2 (Units 7-12) is also available as a free course on edX (certificate available): https://www.edx.org/course/probability-basic-concepts-discrete-random-variables
What you’ll learn:
Description for each Unit: (units 1-6 are available in part 1: https://www.youtube.com/playlist?list=PLNAxrEh0NNf428tm3dhwcbVdviEWpoj6S )
Unit 7: Continuous Random Variables In this unit, we start from the instruction of continuous random variables, then discuss the joint density/CDF and properties of independent continuous random variables.
Unit 8: Conditional Distributions and Expected Values Conditional distributions for continuous random variables, expected values of continuous random variables, and expected values of functions of random variables.
Unit 9: Models of Continuous Random Variables In this unit we will discuss four common distribution models of continuous random variables: Uniform, Exponential, Gamma and Beta distributions.
Unit 10: Normal Distribution and Central Limit Theorem (CLT) Introduction to Normal distribution and CLT, as well as examples of how CLT can be used to approximate models of continuous uniform, Gamma, Binomial, Bernoulli and Poisson.
Unit 11: Covariance, Conditional Expectation, Markov and Chebychev Inequalities Unit
12: Order Statistics, Moment Generating Functions, Transformation of RVs
Instructors and motivated learners: view all of the resources (lessons, examples, problems sets, etc) associated with this video: http://llc.stat.purdue.edu/2017/41600/
In this unit, we start from the instruction of continuous random variables, then discuss the joint density/CDF and properties of independent continuous random variables.
Conditional distributions for continuous random variables, expected values of continuous random variables, and expected values of functions of random variables.
In this unit we will discuss four common distribution models of continuous random variables: Uniform, Exponential, Gamma and Beta distributions.
Introduction to Normal distribution and CLT, as well as examples of how CLT can be used to approximate models of continuous uniform, Gamma, Binomial, Bernoulli and Poisson.