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Related lectures (31)

Page 2 of 4

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

Probability Theory: Events and Tribes

Covers the concept of events and tribes in probability theory, emphasizing the importance of defining valid tribes and their role in modeling random experiments.

Normal Distribution: Characteristics and Examples

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.

Causal Inference: Understanding Treatment Effects

Explores treatment effects, confounding variables, and challenges in interpreting observational study results.

Probability Theory: Fundamentals and Calculations

Covers the basics of probability theory, including events, intersections, unions, and probabilities.

Probability Model Construction

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

Simpson's Paradox: Understanding and Randomized Trials

Explores Simpson's Paradox with a real-world example and emphasizes the significance of randomized trials.

Design of Experiments Basics

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

Statistical Modeling: Fundamentals and Process Factors

Covers the fundamental concept of statistical modeling and process factors for prediction and optimization.

Organic Micropollutant Treatment: Plants in Operation and Pilot Studies

Explores the operation of plants for organic micropollutant treatment, including PAC dosage and pilot study results.