Hypothesis Testing and Non-Parametric Regression

Related lectures (31)

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

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

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

Introduces probability, estimation methods, linear models, testing, and advanced regression techniques.

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

Covers regression with exponential family responses using Generalised Linear Models.

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

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

Explores the distribution theory of least squares estimators in a Gaussian linear model.

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