Goodness-of-Fit Test: Variables and Distributions

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Explains the Chi-Square test for independence hypothesis and its practical applications.

Covers the basics of data analysis, focusing on statistical concepts and correlation measures.

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

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Covers likelihood ratio statistic, confidence intervals, and hypothesis testing concepts.

Explores tools for visualizing categorical data to detect patterns and trends.

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

Covers the multinomial distribution, joint density, marginal distribution, and conditional distribution.

Covers the estimation of theoretical frequencies and independence testing between two characteristics, with examples for practical application.

Explores Poisson GLM for analyzing count data in categorical analysis, focusing on contingency tables and Bayesian inference.