Covers t-tests, p-values calculation, and comparison of coefficients.
Explores correlation limitations, probability, random variables, and statistical inference using a ball-drawing example.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Introduces statistical inference concepts, focusing on parameter estimation, unbiased estimators, and mean estimation using independent random variables.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Covers the concept of mean-square-error inference and optimal estimators for inference problems using different design criteria.
