Understanding Statistics & Experimental Design

Related lectures (31)

Covers z Variable as a Delay Operator, realizable systems, probability theory, stochastic processes, and Hilbert Spaces.

Introduces statistical analysis concepts like boxplot and normal distribution using real data examples.

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

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

Explores correlation limitations, probability, random variables, and statistical inference using a ball-drawing example.

Explores real estate data analysis through descriptive statistics, correlation, regression, and forecasting methods.

Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.

Covers basic probability theory, signal detection theory, statistics, and meta-statistics, explaining effect sizes, power, and hypothesis testing.

Introduces data analysis basics, statistical concepts, Python libraries, and real-world applications.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Covers moments, variance, and expected values in probability and statistics, including the distribution of tokens in a product.

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