Estimating Random Variables

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Statistical Theory: Fundamentals

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

Central Limit Theorem: Illustration and Applications

Explores the Central Limit Theorem and its applications in statistical analysis.

Estimating Random Variables

Covers the estimation of random variables and their functions.

Continuous Random Variables: Basics

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

Expectation Properties: Linearity, Inclusion-Exclusion Formula

Covers the properties of expectation and examples of calculating expectations for random variables.

Probability Distributions: Central Limit Theorem and Applications

Discusses probability distributions and the Central Limit Theorem, emphasizing their importance in data science and statistical analysis.

Conditional Density and Expectation

Covers conditional density, independence of random variables, expectation, and variance calculation.

Elements of Statistics

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

Probability and Stochastic Processes: Fundamentals and Applications

Discusses the fundamentals of probability and stochastic processes, focusing on random variables, their properties, and applications in statistical signal processing.

Probabilities and Statistics

Covers fundamental concepts in probabilities and statistics, including linear regression, exploratory statistics, and the analysis of probabilities.