Scholarly Interests Computational models of human learning and categorization. Judgment and decision-making. Clustering and scaling methods for multivariate data. Statistics expertise and probability problem-solving. Evaluation of educational technology innovations.
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Description
Introduction Two Types of Tree Models Algorithms for Fitting Trees to Data Practical Issues and Applications Some Extensions of Tree Models Discussions and Conclusions Appendix A: Mathematical Programming Appendix B: Availability of Software for Fitting Trees Appendix C: Estimating Fit of a Tree Using Multiple Regression