Regression Methods: Model Building and Inference

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Covers regression methods focusing on spline smoothing and penalised fitting to balance data fidelity and smoothness.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

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

Covers nonparametric regression using kernel-based estimation techniques to model complex relationships between variables.

Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

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

Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.

Introduces regression analysis, covering linear and nonlinear models, Poisson regression, and failure time analysis using various datasets.

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.