Probability and Statistics: Basics and Applications

Related lectures (32)

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Explores confidence intervals, hypothesis testing, and decision-making using test statistics and p-values.

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

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

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

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.

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

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Covers the construction of confidence intervals and approximate critical values in statistical inference.

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

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