General Linear Model: Model Selection

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Explores nested model selection in linear models, comparing models through sums of squares and ANOVA, with practical examples.

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

Explores linear regression analysis of ozone data using statistical models.

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Explores the linear regression model, OLS properties, hypothesis testing, interpretation, transformations, and practical considerations.

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

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

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

Explores Ridge Regression for handling multicollinearity and the LASSO method for model selection.

Explores the IRLS algorithm for weighted least squares estimation in GLM.