Lectures related to Hadamard Design: Introduction and Construction

Linear Regression: Basics and Principle of Least Squares

Covers linear regression basics, focusing on minimizing error using the principle of least squares and includes an ANOVA table and practical example in R.

Linear Regression: Regularization

Covers linear regression, regularization, and probabilistic models in generating labels.

Least Squares Approximation

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

Modeling Response Surfaces in Matlab

Covers modeling response surfaces in Matlab, including building the model, defining the domain, computing values, and analyzing residuals.

Statistical Inference for Bandit Data

Explores statistical inference for bandit data, focusing on personalized treatment actions and challenges of standard estimators.

Heteroskedasticity and Autocorrelation

Explores heteroskedasticity and autocorrelation in econometrics, covering implications, applications, testing methods, and hypothesis testing consequences.

Linear Regression: Estimation and Testing

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.