Regression Diagnostics

Related lectures (31)

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Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.

Explores the assumptions and checking methods for Gaussian linear regression models using residuals and graphical plots.

Introduces linear models, regression, Gaussian distribution, linearity, and model generalization.

Explores linear models, least squares, Gaussian vectors, and model selection methods.

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

Explores the dangers of multicollinearity in linear models and discusses diagnostic methods and remedies.

Explores OLS regressions for house prices, covering outliers, influential observations, model specification, and selection strategies.

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

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

Explores ANOVA, factorial experiments, and model evaluation techniques.