Statistical Modeling: Fundamentals and Process Factors

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Related lectures (31)

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Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.

Observational Studies: Pitfalls and Valid Conclusions

Highlights the pitfalls of observational studies and the importance of randomized experiments.

Probability Model Construction

Explores constructing a probability model, random sampling, variance calculation, and allocation optimization in experiments.

Causal Analysis of Observational Data

Covers causal analysis of observational data, pitfalls, tools for valid conclusions, and addressing confounding variables.

Observational Studies: Pitfalls and Solutions

Explores the challenges of observational studies, emphasizing the importance of randomization and sensitivity analysis in drawing valid conclusions from 'found data'.

Observational Studies: Pitfalls and Solutions

Explores observational studies, pitfalls, solutions, and the importance of valid conclusions in research projects.

Observational Studies: Pitfalls and Solutions

Covers the pitfalls of observational studies, solutions to avoid biases, and the importance of valid conclusions from 'found data'.

Causal Inference: Estimands and Ontologies

Explores causal inference, emphasizing the importance of committing to an ontology for drawing causal inferences and selecting appropriate estimands.

Probability and Statistics for SIC

Delves into probability, statistics, random experiments, and statistical inference, with practical examples and insights.

Design of Experiments Basics

Covers the fundamentals of Design of Experiments (DOE) and experimental strategies.