Introduction to OLS

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Related lectures (32)

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Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Understanding Data Attributes

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

Linear Regression: Least Squares

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

Linear Regression and Logistic Regression

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

Applied Biostatistics: Bivariate Data and Regression Analysis

Covers bivariate data analysis, correlation, and regression techniques, including interpretation of coefficients and least squares geometry.

Linear Regression: Understanding Quantitative Relationships

Covers linear regression, from developing research questions to interpreting R-squared and adding predictors to improve the model.

Regression: Simple and Multiple Linear

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

Supervised Learning in Financial Econometrics

Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.

Linear Regression: Basics and Estimation

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

Linear Regression: Foundations and Applications

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