Covers correlation and cross-correlations in air pollution data analysis, including time series, autocorrelations, Fourier analysis, and power spectrum.
Covers the key concepts of Principal Component Analysis (PCA) and its practical applications in data dimensionality reduction and feature extraction.
Explores scientific misconduct, p-value optimization, and spotting issues with conclusions using real-world examples.
Explains estimation, correlation, and Pearson correlation in statistics, focusing on measuring and describing relationships between variables.
Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.
