Regression Analysis: Disentangling Data

Related lectures (31)

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Explores linear regression, least squares, residuals, and confidence intervals in regression models.

Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.

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

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

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

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

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

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

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.