Extreme Values Detection

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Linear Regression: Estimation and Testing

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

Statistical Analysis: Hypothesis Testing and Distribution Models

Covers statistical analysis techniques, hypothesis testing, and distribution models using R software.

Hypothesis Testing and Confidence Intervals: Key Concepts

Provides an overview of hypothesis testing and confidence intervals in statistics, including practical examples and key concepts.

Statistical Hypothesis Testing: Inference and Interpretation

Explores statistical hypothesis testing, including constructing confidence intervals, interpreting p-values, and making decisions based on significance levels.

Statistics Essentials: The t-test

Introduces the t-test for assessing categorical effects on quantitative outcomes, covering hypothesis testing, assumptions, and alternative tests.

Optimality in Decision Theory: Unbiased Estimation

Explores optimality in decision theory and unbiased estimation, emphasizing sufficiency, completeness, and lower bounds for risk.

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Linear Regression: Estimation and Inference

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

Implicit Generative Models

Explores implicit generative models, covering topics like method of moments, kernel choice, and robustness of estimators.