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Doing Bayesian Data Analysis by John Kruschke

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Chapter 21

Ordinal Predicted Variable

Contents

21.1 Ordinal Probit Regression

21.1.1 What the Data Look Like

21.1.2 The Mapping from Metric x to Ordinal y

21.1.3 The Parameters and Their Priors

21.1.4 Standardizing for MCMC Efficiency

21.1.5 Posterior Prediction

21.2 Some Examples

21.2.1 Why Are Some Thresholds Outside the Data?

21.3 Interaction

21.4 Relation to Linear and Logistic Regression

21.5 R Code

21.6 Exercises

The winner is first, and that’s all that he knows, whether

Won by a mile or won by a nose. But

Second recalls every inch of that distance, in

Vivid detail and with haunting persistence.

Very often the predicted variable is ordinal, such as a rating on a scale from 1 to 5. Rate how much you agree with this statement: “Bayesian methods ...

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