Generalized Linear Models: Exponential Families and Model Construction

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Weighted Least Squares Estimation: IRLS Algorithm

Explores the IRLS algorithm for weighted least squares estimation in GLM.

Modern Regression: Spring Barley Data

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

Modern Regression: Spring Barley Data

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

Modern Regression: Inference and Models

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

Modern Regression: Overdispersion and Model Assessment

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

Inference: Poisson Regression

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

Linear Regression Basics

Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.

Generalised Linear Models: Regression with Exponential Family Responses

Covers regression with exponential family responses using Generalised Linear Models.

Linear Regression Basics

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

Linear Models: Least Squares

Explores linear models, least squares, Gaussian vectors, and model selection methods.