Learn Roulette Video Source & Info:

A Bernoulli distribution shows probabilities in a random experiment with only 2 possible outcomes (either a “success” or a “failure”) in which the probability of a success is constant. Binomial distributions aggregate the data from single Bernoulli events. This lesson uses the example of a Roulette wheel to outline the conditions for a binomial distribution and show what we else can learn from both distributions: mean, variance, standard deviation.

Olanrewaju Michael Akande is a professor in the Department of Statistical Science at Duke University. He covers Discrete and Continuous Random Variables and The Central Limit Theorem. He earned both his Ph.D. in Statistical Science and his Masters of Science (M.S.) in Statistical and Economic Modeling from Duke University. His research focuses on developing statistical methodology for handling missing and faulty data, particularly in the social sciences.

Chapters:
0:00 Bernoulli Experiment
1:26 Binomial Distribution
2:36 Probability Mass Function (PMF)
6:10 Why the Binomial Formula Works
8:43 Example
10:49 Mean, Variance, Standard Deviation Formulas

This lesson is excerpted from Outlier’s Intro to Statistics course. Learn more about this course and check out our full catalog at https://www.outlier.org.

About @OutlierOrg :
Outlier.org (from the co-founder of MasterClass) has developed the world’s best online, university-level courses, taught by some of the most celebrated educators in the world. Our courses employ engaging content and cutting-edge educational technology to create an immersive, student-friendly learning experience complete with transferable credits. Outlier is on a mission to make higher education more accessible, equitable, and affordable for everyone.

#Statistics #MichaelAkande #BinomialDistribution #Outlier

Source: YouTube