Statistical Inference: Concepts and Applications

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Related lectures (31)

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Probabilities and Statistics: Key Theorems and Applications

Discusses key statistical concepts, including sampling dangers, inequalities, and the Central Limit Theorem, with practical examples and applications.

Probability and Statistics for SIC

Delves into probability, statistics, random experiments, and statistical inference, with practical examples and insights.

Statistical Inference: Approximate Critical Values and Confidence Intervals

Covers the construction of confidence intervals and approximate critical values in statistical inference.

Testing: t-tests

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

Linear Regression: Estimation and Inference

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Generalization Error

Explores tail bounds, information bounds, and maximal leakage in the context of generalization error.

Statistics: Exploratory Data Analysis

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

Confidence Intervals: Definition and Estimation

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Estimating R: Example 100

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

Linear Regression: Estimation and Testing

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.