Statistical Modelling & Probability Basics

Related lectures (29)

Page 3 of 3

Introduces the basics of conditional expectation, covering definitions, properties, and examples in the context of random variables.

Explains the calculation of conditional probability with definitions and examples.

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

Explores the Law of Total Probability and its applications in real-world scenarios, introducing key concepts in probability theory.

Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.

Covers the fundamentals of probability theory, including conditional probability, Bayes' rule, and random variables.

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