Category
Regression analysis
Related lectures (969)
Linear Regression: Simple
Introduces simple linear regression, properties of residuals, variance decomposition, and the coefficient of determination in the context of Okun's law.
Linear Regression Basics
Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.
Linear Regression Basics
Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
Linear Regression: Multicollinearity, Outliers, Model Specification
Covers multicollinearity, outliers, model specification, and practical strategies in linear regression.
Weighted Least Squares Estimation: IRLS Algorithm
Explores the IRLS algorithm for weighted least squares estimation in GLM.
Linear Regression: Beyond the Basics
Explores advanced concepts in linear regression models, including multicollinearity, hypothesis testing, and handling outliers.
Linear Regression Basics
Introduces the basics of linear regression, covering OLS approach, residuals, hat matrix, and Gauss-Markov assumptions.
Linear Regression: Fundamentals and Applications
Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.
Instrumental Variables: Addressing Measurement Error and Reverse Causality
Explores how instrumental variables correct biases from measurement error and reverse causality in regression models.
Basics of Linear Regression
Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.