Lectures related to Regression Methods: Spline Smoothing

Regression Methods: Model Building and Inference

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

Regression Methods: Model Building and Inference

Covers analysis of variance, model building, variable selection, and function estimation in regression methods.

Splines: Fundamentals and Applications

Explores B-splines, natural cubic splines, and smoothing splines in regression problems and their practical applications.

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Linear Regression: Simple

Introduces simple linear regression, properties of residuals, variance decomposition, and the coefficient of determination in the context of Okun's law.

ANOVA: Partitioning Total SS

Covers ANOVA method, focusing on partitioning total sum of squares into treatment and error components, mean square calculations, Fisher statistic, and F-distribution.

Modern Regression: Spring Barley Data

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

Non-parametric Regression: Smoothing Techniques

Explores non-parametric regression techniques, including splines, bias-variance tradeoff, orthogonal functions, wavelets, and modulation estimators.

Model Diagnostics: Outliers, Leverage, and Influential Observations

Explores outliers, leverage, and influential observations in statistical models, including methods for detection and assessment.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Modern Regression: Overdispersion and Model Assessment

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

Linear and Weighted Regression: Optimal Parameters and Local Solutions

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.

Data-Driven Modeling: Regression

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

Nonparametric Regression: Kernel-Based Estimation

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

Linear Regression: Understanding Quantitative Relationships

Covers linear regression, from developing research questions to interpreting R-squared and adding predictors to improve the model.

Supervised Learning: Regression Methods

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Inference: Model Checking

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

Regression Analysis: Disentangling Data

Covers regression analysis for disentangling data using linear regression modeling, transformations, interpretations of coefficients, and generalized linear models.

Bivariate Data Analysis: Correlation and Regression

Explores bivariate data analysis, correlation, and regression techniques for model assessment.

Linear Regression: Model Adjustment and Parameter Estimation

Explains the decomposition of total sum of squares, model adjustment, and parameter estimation in linear regression.