Leverage (statistics)

Related lectures (19)

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

Explores regression methods, covering model building, diagnostics, inference, and analysis of variance.

Introduces the basics of linear regression, covering OLS approach, residuals, hat matrix, and Gauss-Markov assumptions.

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

Explains the least-squares regression line, correlation coefficients, outliers, influential points, and residuals in regression models.

Introduces k-Nearest Neighbors for classification and feature expansion to handle nonlinear data through transformed inputs.

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

Covers multicollinearity, outliers, model specification, and practical strategies in linear regression.

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