Multicollinearity and Model Fit Analysis

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Related lectures (32)

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Multicollinearity: Dangers and Remedies

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

Linear Regression Model

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

Model Selection Criteria: AIC, BIC, Cp

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

Linear Regression: Beyond the Basics

Explores advanced concepts in linear regression models, including multicollinearity, hypothesis testing, and handling outliers.

General Linear Model: Model Selection

Explores the General Linear Model, significance testing, model selection, and parameter inference.

Linear Regression: Estimation and Inference

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

Linear Regression: Ozone Data Analysis

Explores linear regression analysis of ozone data using statistical models.

Linear Regression: Multicollinearity, Outliers, Model Specification

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

Orthogonality and Projection

Covers orthogonality, scalar products, orthogonal bases, and vector projection in detail.

Linear Regression Basics

Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.