Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Explores independence and covariance between random variables, discussing their implications and calculation methods.
Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.
Explores correlation limitations, probability, random variables, and statistical inference using a ball-drawing example.
Explores the correct exponent for the end-to-end distance in polymer chains and its implications on chain correlations.
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Explores multivariate Gaussian laws, covering probabilities, correlations, and conditional distributions in bivariate cases.
Covers correlation and cross-correlations in air pollution data analysis, including time series, autocorrelations, Fourier analysis, and power spectrum.
