Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.
Introduces the t-test for assessing categorical effects on quantitative outcomes, covering hypothesis testing, assumptions, and alternative tests.
Covers the essentials of linear regression, focusing on using multiple quantitative explanatory variables to predict a quantitative outcome.
Explores the linear regression model, OLS properties, hypothesis testing, interpretation, transformations, and practical considerations.
Introduces descriptive statistics, uncertainty quantification, and variable relationships, emphasizing the importance of statistical interpretation and critical analysis.
