Lectures related to Folded normal distribution

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.

Introduces inferential statistics, covering sampling, central tendency, dispersion, histograms, z-scores, and the normal distribution.

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

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

Covers Cramer's theorem and Hoeffding's inequality in the context of the large deviations principle.

Explores statistical models, families of distributions, transformations, and their applications in probability theory.

Covers the theory of sampling, focusing on statistics for mathematicians.

Presents the proof of the Central Limit Theorem using Lindeberg's principle.

Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.

Covers the process of making pizza, sampling, averages, dispersion, residuals, and normal distribution.

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.