Continuous Random Variables: Distributions and Expectation

Related lectures (30)

Page 3 of 3

Covers the normal distribution, including its properties and calculations.

Explains moments, expectation, variance, and quantiles in probability distributions with examples like Poisson and exponential distributions.

Covers probability distributions, moments, and continuous random variables.

Explores continuous random variables, cdf, pdf, convolution, and sum of dice.

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

Explores independent and identically distributed random variables, convergence, and distribution properties.

Covers random variables, sample spaces, probability distributions, functions, expected value, variance, and estimations.

Discusses variance and covariance calculations, transformations of random variables, and mixtures in probability and statistics.

Explores PDFs and CDFs for discrete and continuous random variables, including Gaussian, uniform, and exponential distributions.

Covers random variables, probability functions, expectations, variances, and joint distributions.