Probability Distributions: Central Limit Theorem and Applications

Related lectures (30)

Page 2 of 3

Covers properties and calculations related to the normal distribution, including probabilities and quantiles.

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.

Covers the calculation of expectation and variance for different types of random variables, including discrete and continuous ones.

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Explores continuous random variables and their properties, including support and cumulative distribution functions.