Linear Mixed Model

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Related lectures (32)

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Regression: Linear Models

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

Multilevel Models: Part 2

Explores advanced techniques in multilevel modeling, including fitting separate models, estimating coefficients, and checking residuals for model evaluation.

Regression Methods: Model Building and Inference

Covers Inference, Model Building, Variable Selection, Robustness, Regularised Regression, Mixed Models, and Regression Methods.

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.

Modern Regression: Spring Barley Data

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

Statistical Inference: Linear Models

Explores statistical inference for linear models, covering model fitting, parameter estimation, and variance decomposition.

Logistic Regression: Modeling Binary Response Variables

Explores logistic regression for binary response variables, covering topics such as odds ratio interpretation and model fitting.

Likelihood Estimation and Least Squares

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

Regression: Simple and Multiple Linear

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

Modern Regression: Overdispersion and Model Assessment

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