Explains estimation, correlation, and Pearson correlation in statistics, focusing on measuring and describing relationships between variables.
Explores scientific misconduct, p-value optimization, and spotting issues with conclusions using real-world examples.
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Introduces descriptive statistics, uncertainty quantification, and variable relationships, emphasizing the importance of statistical interpretation and critical analysis.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Covers correlation and cross-correlations in air pollution data analysis, including time series, autocorrelations, Fourier analysis, and power spectrum.
Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.
Explores mutual information for quantifying statistical dependence between variables and inferring probability distributions from data.
Explores independence and covariance between random variables, discussing their implications and calculation methods.
