Elements of Statistics: Exercises

Related lectures (30)

Page 3 of 3

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

Explores convergence in probability, concentration inequalities, laws of large numbers, and properties of distributions.

Explores conditional density, expectations, and independence of random variables with practical examples.

Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.

Covers the central limit theorem, showing how random processes converge to a normal distribution.

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

Covers the modes of convergence of random variables and the Central Limit Theorem, discussing implications and approximations.

Covers fundamental concepts in probability and statistics, including distributions, properties, and expectations of random variables.

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

Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.