This module introduces fundamental concepts in probability distributions, including random variables, distribution functions, and key summary measures. Learners will explore common distributions such as binomial, normal, and Poisson, and understand how they model real-world uncertainty. The module builds a strong foundation for applying probabilistic thinking in statistical analysis and decision-making.

This module explores the use of Bayesian inference to analyze real-world data, focusing on probability calculations, parameter estimation, and the application of prior distributions. Learners will gain insight into statistical methods for evaluating equality and income distribution through practical examples. The content covers both theoretical foundations and real-world applications of inferential statistics.

This module explores the statistical modeling of income effects on health outcomes using Bayesian inference and advanced analytical techniques. Learners will gain an understanding of posterior distributions, prior influences, and model comparison methods. The focus is on applying these tools to real-world data to draw meaningful inferences about relationships between income and health.

This module introduces learners to advanced statistical modeling techniques, including generalized linear models, handling of missing data, and the interpretation of model parameters. It covers how to build and analyze models with multiple variables and factors, and explores alternative distributions beyond the normal distribution.

This module covers the application of Bayesian regression models to analyze ordinal and categorical response variables from questionnaires. Learners will gain skills in modeling non-quantitative data, interpreting results, and evaluating model fit. The focus is on practical techniques for handling real-world survey data.

This module delves into advanced statistical modeling techniques, including mixed effect models, Bayesian methods, and Markov Chain Monte Carlo (MCMC) for posterior estimation. It explores practical applications of these methods in real-world studies and introduces concepts like partial pooling and multinomial logit models. Learners will gain a deeper understanding of statistical inference and modeling in complex data scenarios.