Measuring Learning: Experimental Design

Related lectures (32)

Page 3 of 4

Covers factorial experimental design, 2-way ANOVA, interaction plots, and unbalanced designs.

Explores replicates, ANOVA, and factor elimination in full factorial designs using ANOM tables and model equations.

Explores demand forecasting methods and cognitive biases affecting decision-making processes.

Covers the transition from a comparator to an amplifier, focusing on the different stages and components involved.

Covers the basics of ANOVA using a case study on sleeping pill effectiveness.

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

Explores the significance of anonymity in avoiding judgment based on personal characteristics and the need to balance it with accountability.

Explores how instrumental variables correct biases from measurement error and reverse causality in regression models.

Covers criteria for estimating parameters, emphasizing the importance of consistency, bias, variance, and efficiency of estimators.

Explores modeling qualitative factors in experimental design, including pseudo-tensors, Latin squares, and ANOVA analysis.