Lectures related to Model Specification: The Error Term

Covers the binary choice model, error term assumptions, specific constants, invariances, and distribution properties.

Probabilities and Statistics: Key Theorems and Applications

Discusses key statistical concepts, including sampling dangers, inequalities, and the Central Limit Theorem, with practical examples and applications.

Calculations of Expectation

Covers the calculation of expectation and variance for different types of random variables, including discrete and continuous ones.

Advanced Probabilities: Random Variables & Expected Values

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Estimating R: Moments of a Distribution

Explains the importance of moments in measuring distribution properties, such as expectation and variance.

Convergence of Random Variables

Explores the convergence of random variables, the law of large numbers, and the distribution of failure time.

Convergence of Random Variables

Explores different modes of convergence for random variables.

Elements of Statistics: Probability and Random Variables

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Constructing Real Numbers: Decimal Expansion

Covers constructing real numbers through decimal expansion and the concept of discrete and continuous random variables.

Mixture Models: Simulation-based Estimation

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Continuous Random Variables: Basic Ideas

Explores continuous random variables and their properties, including support and cumulative distribution functions.

Probability Distributions in Environmental Studies

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

Vectors of Random Variables: Empirical Distributions

Discusses vectors of random variables and empirical distributions, including their properties and significance in statistics.

Probability Distributions: Central Limit Theorem and Applications

Discusses probability distributions and the Central Limit Theorem, emphasizing their importance in data science and statistical analysis.

Random Variables: Basics and Examples

Explains random variables, distributions, and Bernoulli trials with coin flip examples.

Probability and Statistics

Covers moments, variance, and expected values in probability and statistics, including the distribution of tokens in a product.

Probability and Statistics

Covers probability distributions, moments, and continuous random variables.

Continuous Random Variables

Covers continuous random variables, probability density functions, and distributions, with practical examples.

Statistics: Exploratory Data Analysis

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

Extreme Value Models: Technical Derivation

Explores the technical derivation and properties of Multivariate Extreme Value models.