Statistics Essentials: Two-way ANOVA

Related lectures (31)

Page 3 of 4

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

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Explores the Weibull model's application to random data and its significance in analyzing material strength and failure probability.

Explores alternative specific variance in mixture models and discusses identification issues and model comparisons using 500 draws.

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Covers risk and return measures, unbiasedness, and consistency of estimators.

Introduces maximum likelihood estimation for statistical parameter estimation, covering bias, variance, and mean squared error.

Explores outliers, leverage, and influential observations in statistical models, including methods for detection and assessment.

Covers basic probability theory, ANOVA, experimental design, and statistical reporting errors.

Explores nested model selection in linear models, comparing models through sums of squares and ANOVA, with practical examples.