Statistics

Related lectures (30)

Page 3 of 3

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Explores common probability distributions, special distributions, and entropy concepts.

Covers variational inference and neural networks for classification tasks.

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

Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.

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

Covers statistical hypothesis testing, confidence intervals, p-values, and significance levels in hypothesis testing.

Covers fundamental statistics concepts, including estimation theory, distributions, and the law of large numbers, with practical examples.

Explores the linear regression model, OLS properties, hypothesis testing, interpretation, transformations, and practical considerations.

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.