Red bus/Blue bus paradox

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Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.

Covers z Variable as a Delay Operator, realizable systems, probability theory, stochastic processes, and Hilbert Spaces.

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.