Course

MATH-408: Regression methods

Lectures in this course (33)

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

Covers iterative weighted least squares, Poisson regression, mixed models, and likelihood ratio statistic.

Covers the IWLS algorithm for obtaining MLEs in regression models and discusses the likelihood ratio statistic and deviance in model fitting.

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

Explores regression methods, covering model building, diagnostics, inference, and analysis of variance.

Covers exponential families, model construction, and canonical link functions in Generalized Linear Models.

Introduces Generalized Linear Models, showcasing examples with different distributions and real-world data.

Covers estimation of unknown parameters, analyzing model fit, bird prey response, and model diagnostics.

Explores regression modeling, emphasizing the importance of interpretation and prediction in model building.

Covers the analysis of chimpanzee data on learning times and explores generalized linear models and logistic regression.

Explores variable selection methods and the bias-variance trade-off in statistical modeling.

Explores binary response interpretation, link functions, logistic regression, and model selection using deviances and information criteria.

Covers iterative weighted least squares, model checking, Poisson regression, and fitting multinomial models using Poisson errors.

Covers jacamar data analysis, smoking data models, and challenges with log-linear models in visual impairment data.

Explores marginal models in modern regression, emphasizing interpretation and application in statistical analysis.

Explores overdispersion, model assessment, and regression techniques for count data.

Explores random effects, model checking, and nested vs. crossed effects in modern regression modeling.

Covers the linear mixed model, including fixed and random effects, estimation, and inference techniques.

Explores maximum likelihood estimation, profile log likelihood, inference on coefficients, quasi-likelihood, model comparison, and REML method.

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