Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Explores dependence in random vectors, covering joint density, conditional independence, covariance, and moment generating functions.
Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.
Introduces descriptive statistics, uncertainty quantification, and variable relationships, emphasizing the importance of statistical interpretation and critical analysis.
