Lectures related to Log-normal distribution

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

Probability Distributions in Environmental Studies

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

Mixture models: taste heterogeneity

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

Review Session: Module 1

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

Normal Distribution: Characteristics and Examples

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

Multivariate Statistics: Normal Distribution

Covers the multivariate normal distribution, properties, and sampling methods.

Latent Variable Models

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

Sampling Theory: Statistics for Mathematicians

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

Estimation and Confidence Intervals

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

Monte-Carlo Integration

Covers Monte-Carlo integration, simulation, and transforming draws from different distributions.

Multivariate Statistics: Conditional Distributions

Covers conditional distributions and correlations in multivariate statistics, including partial variance and covariance, with applications to non-normal distributions.

Normal Distribution: Properties and Calculations

Covers the normal distribution, including its properties and calculations.

Interval Estimation

Covers the construction of confidence intervals for a normal distribution with unknown mean and variance.

Statistics: Hypothesis Testing & Confidence Intervals

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

Continuous Random Variables

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Laplace Approximation: Integral Estimation

Explores Laplace approximation for integral estimation using smooth convex functions and Gaussian distribution.

Statistical Analysis: Boxplot and Normal Distribution

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

Probability and Statistics: Data Modeling and Analysis

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

Normal Distribution: Basics and Applications

Covers the basics of the normal distribution and its applications in probability calculations.

Moment Generating Function and Multivariate Normal Distribution

Explores moment generating functions and multivariate normal distributions in probability and statistics.