Generalized Linear Models: Exponential Families and Model Construction

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Statistical Justification of Least Squares

Explores the statistical justification of Least Squares and Generalized Linear Models.

Likelihood Estimation and Least Squares

Introduces simple and multiple normal linear regression, and maximum likelihood estimation with practical examples.

Regression: Linear Models

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

Generalized Linear Models

Covers Generalized Linear Models, likelihood, deviance, link functions, sampling methods, Poisson regression, over-dispersion, and alternative regression models.

Generalized Linear Models: A Brief Review

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

Chimpanzee Data Analysis

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

Generalized Linear Regression: Classification

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Inference: Model Checking

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

Geometry and Least Squares

Discusses the geometry of least squares, exploring row and column perspectives, hyperplanes, projections, residuals, and unique vectors.

Linear Regression: Maximum Likelihood Approach

Covers linear regression topics including confidence intervals, variance, and maximum likelihood approach.