Linear Regression: Model Adjustment and Parameter Estimation
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Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.
Introduces simple linear regression, properties of residuals, variance decomposition, and the coefficient of determination in the context of Okun's law.
Explores advanced techniques in multilevel modeling, including fitting separate models, estimating coefficients, and checking residuals for model evaluation.
