Variance, Covariance, and Correlation

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Related lectures (30)

Page 3 of 3

Explores stationarity in stochastic processes, showcasing how statistical characteristics remain constant over time and the implications on random variables and Fourier transforms.

Stochastic Models for Communications

Covers random vectors, stochastic models, functions, matrices, and expectations in communication systems.

Stochastic Processes: Basics & Stationarity

Covers signal generation, statistical relations, stationarity, white noise, and orthogonality in stochastic processes.

Joint Distributions

Explores joint distributions, marginal laws, covariance, correlation, and variance properties.

Law of Large Numbers: Statistics

Explains the Law of Large Numbers and its application to random variables.

Law of Large Numbers: Strong Convergence

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

Conditional Density and Expectation

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

Describing Data: Statistics and Hypothesis Testing

Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.

Random Vectors & Distribution Functions

Covers random vectors, joint distribution, conditional density functions, independence, covariance, correlation, and conditional expectation.

Second Moment Method

Explores the Second Moment Method and variance of random variables, including covariance and independence.