Lectures related to Probabilities and Statistics

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

Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.

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

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

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Explores joint random variables, conditional density, and independence in probability and statistics.

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.

Covers the normal distribution, including its properties and calculations.

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

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

Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.

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

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

Covers fundamental concepts of probability and statistics, focusing on data analysis, graphical representation, and practical applications.

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

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

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

Explores moment-generating functions, linear combinations, and normality of random variables.

Explores confidence intervals, hypothesis testing, and decision-making using test statistics and p-values.