Introduces the t-test for assessing categorical effects on quantitative outcomes, covering hypothesis testing, assumptions, and alternative tests.
Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.
Introduces simple linear regression, properties of residuals, variance decomposition, and the coefficient of determination in the context of Okun's law.
Covers random variables, sample spaces, probability distributions, functions, expected value, variance, and estimations.
Covers the concept of mean-square-error inference and optimal estimators for inference problems using different design criteria.
Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.
Explores model selection in statistics, discussing principles, probabilistic models, characteristics evaluation, and data visualization methods.
