Covers correlation and cross-correlations in air pollution data analysis, including time series, autocorrelations, Fourier analysis, and power spectrum.
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.
Explains estimation, correlation, and Pearson correlation in statistics, focusing on measuring and describing relationships between variables.
Explores autocorrelation, periodicity, and spurious correlations in time series data, emphasizing the importance of understanding underlying processes and cautioning against misinterpretation.
Explores scientific misconduct, p-value optimization, and spotting issues with conclusions using real-world examples.
Explores portfolio rate of return, valuation, risk characterization, and historical performance, emphasizing diversification benefits and mean-variance analysis.
Covers the key concepts of Principal Component Analysis (PCA) and its practical applications in data dimensionality reduction and feature extraction.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
