Heteroskedasticity and Autocorrelation

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Explains least squares approximation for finding best fit lines or curves to data points.

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Introduces regression analysis for multivariate data modeling, covering matrix algebra, interpretation of coefficients, and test intervals.

Covers Ordinary Least Squares Regression analysis and regression model restrictions.

Explores bivariate data analysis in applied biostatistics, covering correlation, regression, model selection, and diagnostics.

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

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Discusses the geometry of least squares, exploring row and column perspectives, hyperplanes, projections, residuals, and unique vectors.

Explores linear regression using the method of least squares to fit data points with the equation y = ax + b.