Generalized Linear Models

Related lectures (30)

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

Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.

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

Covers the normal distribution, including its properties and calculations.

Introduces probability, estimation methods, linear models, testing, and advanced regression techniques.

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

Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.

Explores convergence in probability, concentration inequalities, laws of large numbers, and properties of distributions.

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

Explores linear regression analysis of ozone data using statistical models.