Gaussian Linear Regression: Assumptions and Residuals

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Explores linear regression using the method of least squares to fit data points with the equation y = ax + b.

Delves into linear regression, emphasizing least squares estimation, residuals, and variance.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

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

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Covers the analysis of various data attributes and linear regression models.

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

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

Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.