Probability and Statistics: Data Modeling and Analysis

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Related lectures (31)

Page 3 of 4

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Elements of Statistics: Exercises

Covers exercises on Bayes' Theorem, moment generating functions, photon counts, disease probabilities, and distribution properties.

Confidence Intervals and Hypothesis Testing

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

Normal Distribution: Characteristics and Z-scores

Explores normal distribution characteristics, Z-scores, probability in inferential statistics, sample effects, and binomial distribution approximation.

Probability and Statistics

Covers fundamental concepts in probability and statistics, including the law of total probability, Bayes' theorem, and independence of events.

Probability Distributions

Covers various probability distributions and their applications in real-world scenarios.

Probability and Statistics

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

Testing: t-tests

Covers t-tests, p-values calculation, and comparison of coefficients.

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.