Regression: Linear Models

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Related lectures (32)

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Linear Systems: Modeling and Identification

Covers auto-encoders, linear systems modeling, system identification, and recursive least squares.

Confidence Bounds: Key Concepts and Variance Analysis

Covers key assumptions, variance analysis, and confidence intervals in linear regression.

Linear Regression

Introduces linear regression, covering line fitting, training, gradients, and multivariate functions, with practical examples like face completion and age prediction.

Applied Biostatistics: Bivariate Data Analysis

Explores bivariate data analysis in applied biostatistics, covering correlation, regression, model selection, and diagnostics.

Linear Regression: Estimation and Testing

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Linear Regression Testing

Explores least squares in linear regression, hypothesis testing, outliers, and model assumptions.

Linear Regression: Ozone Data Analysis

Explores linear regression analysis of ozone data using statistical models.

Regression II

Delves into regression analysis, emphasizing distributional checks, weighted least squares, and hypothesis testing.

Linear Regression Model

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

Linear Regression: Regularization

Covers linear regression, regularization, and probabilistic models in generating labels.