Probability and Statistics

Related lectures (29)

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Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

Covers probability, statistics, independence, covariance, correlation, and random variables.

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

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

Explains conditional probability through dice and coin examples.

Covers the basics of probability theory, random variables, joint probability, and inference.

Covers conditional probability distributions and introduces the concept of conditional expected value.

Introduces fundamental probability concepts, including events, complements, conditional probability, and random variables.

Explores variance, covariance, and practical applications in statistics and probability.