Data Exploration: Normal Distribution

Related lectures (31)

Page 3 of 4

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

Covers properties and calculations related to the normal distribution, including probabilities and quantiles.

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

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

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

Covers hypothesis testing, confidence intervals, data distributions, and statistical significance in data analysis.

Introduces multivariate statistics, covering normal distribution properties and characteristic functions.

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

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

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