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Discussion Summary Form
Please use this form to submit the short summary of assigned discussion questions in Data Science for Psychology & Neuroscience (CMU 85-432, 85-732).
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The value of openness
What is a theory?
Models as testable hypotheses
Data as objects & architectures
Techniques for data cleansing
Visualization as analysis
Visualization through human eyes
The bias-variance tradeoff
Linear models
The ordinary least squares solution
Limits of linear regression
Mixed effects models
Classifiers
The beauty of kNN
Cross validation
Resampling methods
Mediation, moderation, & graphs
Power analysis via simulations
Selecting the "best" model
Regularized regression
Principal component methods
Decision trees
Bagging, random forests, & boosting
Reconsidering the p-value
Bayes factor: accepting the null
Errors and inferences
Telling your data story
Theories as social constructs
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