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Multivariate statistics

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

Regression: Linear Models

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

Modern Regression: Smoothing and Modelling Choices

Explores roughness penalty, band matrices, and Bayesian inference in regression smoothing.

Linear Mixed Model

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

Natural Cubic Splines: Optimization and Penalization

Explores the optimization and penalization of natural cubic splines, including roughness penalties and Bayesian inference.

Modern Regression: Random Effects and Model Checking

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

Generalized Additive Models: Applications and Techniques

Explores Generalized Additive Models, covering basics, smooth functions, penalties, practical examples in R, and linear mixed models.

Modern Regression: Spring Barley Data

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

Non-Negative Definite Matrices and Covariance Matrices

Covers non-negative definite matrices, covariance matrices, and Principal Component Analysis for optimal dimension reduction.

Modern Regression: Spring Barley Data

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