Ordinary Least Squares Regression Analysis

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Linear Regression: Basics and Estimation

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

Examples of decision models for Supervised learning

Covers examples of decision models for supervised learning, including regression, classification, ranking pairs, and sequence decoding for OCR models.

Regression II

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

Machine Learning Fundamentals: Structure Discovery, Classification, Regression

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.

Linear Regression: Beyond the Basics

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

Linear Regression Basics

Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.

Linear Models: Ridge, OLS and LASSO

Covers linear models like Ridge, OLS, and LASSO, explaining singular values and regression analysis.

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Linear Regression: Estimation and Testing

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

Instrumental Variables: Intuition and Estimation

Covers instrumental variables, addressing endogeneity issues in regression analysis through estimation techniques and practical examples.