Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Covers continuous random variables, probability density functions, and distributions, with practical examples.
Covers fundamental concepts in probabilities and statistics, including linear regression, exploratory statistics, and the analysis of probabilities.
Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.
Covers the Central Limit Theorem and its application to random variables, proving convergence to a normal distribution.
Covers moment and cumulant generating functions for random variables, enabling calculation of moments and cumulants.
Covers the central limit theorem, showing how random processes converge to a normal distribution.
Covers Monte-Carlo integration, simulation, and transforming draws from different distributions.
Introduces the basics of risk analysis and management in civil engineering, covering distributions, statistical reminders, and mathematical interpretation techniques.
