Lecture
Linear Regression Basics
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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: Statistical Inference and Regularization
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Regression: Simple and Multiple Linear
Covers simple and multiple linear regression, including least squares estimation and model diagnostics.
Probabilistic Models for Linear Regression
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Linear Regression: Ozone Data Analysis
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Logistic Regression: Vegetation Prediction
Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.
Regression: Linear Models
Explores linear regression, least squares, residuals, and confidence intervals in regression models.
Weighted Least Squares Estimation: IRLS Algorithm
Explores the IRLS algorithm for weighted least squares estimation in GLM.
Basics of Linear Regression
Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.
Model Selection Criteria: AIC, BIC, Cp
Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.