Folded normal distribution

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Explores the Central Limit Theorem and its applications in statistical analysis.

Introduces basic bounds, LLN, and CLT in probability theory, emphasizing convergence to normal distribution.

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

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.

Introduces statistical analysis concepts like boxplot and normal distribution using real data examples.

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.