Modern Regression: Spring Barley Data

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Modern Regression: Spring Barley Data

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

Modern Regression: Overdispersion and Model Assessment

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

Weighted Least Squares Estimation: IRLS Algorithm

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

Linear Regression Basics

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

Regression: Simple and Multiple Linear

Covers simple and multiple linear regression, including least squares estimation and model diagnostics.

Linear Regression Basics

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

Modern Regression: Inference and Models

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

Inference: Model Checking

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

Linear Regression: Basics and Estimation

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

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.