Complex normal distribution

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Explores PDF forms, statistics, boxplots, density curves, and data analysis methods.

Explores constructing confidence intervals and MLE limit theorems for large samples.

Covers the construction of confidence intervals for a normal distribution with unknown mean and variance.

Covers t-tests, p-values calculation, and comparison of coefficients.

Covers the construction of approximate confidence intervals using the central limit theorem for large sample sizes.

Covers the normal distribution, inferential statistics, probability, and the binomial distribution in the context of the 'Dishonest Gambler Problem'.

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.

Explores mixture models in discrete choice and random parameters estimation results.

Explores Poisson statistics, examples of earthquakes and lightning strikes, dark current, and readout noise in photosensitive devices.

Explains how q = rw defines a Poisson process and its intensity.

Covers an example of Bayesian parameter estimation and the trade-off between bias and variance in supervised learning.

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