Regression Analysis: Interpretation and Applications
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Covers linear regression basics, focusing on minimizing error using the principle of least squares and includes an ANOVA table and practical example in R.
Explores Ridge and Lasso Regression for regularization in machine learning models, emphasizing hyperparameter tuning and visualization of parameter coefficients.
Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.
Explores heteroskedasticity and autocorrelation in econometrics, covering implications, applications, testing methods, and hypothesis testing consequences.
