Linear Regression Basics

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Related lectures (31)

Page 1 of 4

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

Basics of linear regression model

Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.

Linear Regression: Statistical Inference and Regularization

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

Basics of Linear Regression

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

Instrumental Variables: Addressing Measurement Error and Reverse Causality

Explores how instrumental variables correct biases from measurement error and reverse causality in regression models.

Linear Regression: Estimation and Inference

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

Weighted Least Squares Estimation: IRLS Algorithm

Explores the IRLS algorithm for weighted least squares estimation in GLM.

Autocorrelation and Instrumental Variables

Explores autocorrelation, instrumental variables, and challenges in regression analysis.

Linear Regression: Least Squares

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

Linear Regression: Basics and Estimation

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