Modern Regression: Spring Barley Data

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Related lectures (32)

Page 2 of 4

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

Generalized Linear Models

Covers Generalized Linear Models, likelihood, deviance, link functions, sampling methods, Poisson regression, over-dispersion, and alternative regression models.

Generalized Linear Regression: Classification

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Regression: Simple and Multiple Linear

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

Regression: Linear Models

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

Modern Regression: Statistical Models and Data Analysis

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

Nonlinear Machine Learning: k-Nearest Neighbors and Feature Expansion

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Likelihood Inference

Covers iterative weighted least squares, Poisson regression, mixed models, and likelihood ratio statistic.

Linear and Weighted Regression: Optimal Parameters and Local Solutions

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

Robust Regression: Methods and Applications

Explores robust and resistant methods in linear models, emphasizing the importance of handling extreme observations and the implications of robustness in regression models.