Generalized Linear Models

Related lectures (30)

Page 3 of 3

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

Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.

Covers iterative weighted least squares, Poisson regression, and Bayesian analysis of spring barley data using mixed models.

Explores Generalized Linear Models theory, including logistic and Poisson regression, model evaluation, and coefficient tests.

Introduces regression analysis, covering linear and nonlinear models, Poisson regression, and failure time analysis using various datasets.

Covers inference, weighted least squares, spring barley data analysis, and smoothing techniques.

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.

Covers iterative weighted least squares, generalized linear models, and model checking.

Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.

Explores common probability distributions, special distributions, and entropy concepts.