Risk, Variation, and Uncertainty

Related lectures (29)

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Explores conditional density, expectations, and independence of random variables with practical examples.

Introduces key concepts in probability and statistics, covering random experiments, events, intersections, unions, and more.

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

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

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

Discusses estimation and propagation of uncertainty in random variables and the importance of managing uncertainty in statistical analysis.

Covers moments, variance, and expected values in probability and statistics, including the distribution of tokens in a product.

Explores correlation limitations, probability, random variables, and statistical inference using a ball-drawing example.

Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.