Dissertation/Thesis Abstract

Data mining techniques for constructing jury selection models
by Espy, John, M.S., California State University, Long Beach, 2013, 48; 1527548
Abstract (Summary)

Jury selection can determine a case before it even begins. The goal is to predict whether a juror rules for the plaintiff or the defense in the medical malpractice trials that are conducted, and which variables are significant in predicting this. The data for the analysis were obtained from mock trials that simulated actual trials, with possible arguments from the defense and the plaintiff with ample discussion time. These mock trials were supplemented by surveys that attempted to capture the characteristics and attitudes of the mock juror and the case at hand. The data were modeled using the logistic regression as well as decision trees and neural networks techniques.

Indexing (document details)
Advisor: Korosteleva, Olga
Commitee: Ebneshahrashoob, Morteza, Safer, Alan
School: California State University, Long Beach
Department: Mathematics and Statistics
School Location: United States -- California
Source: MAI 52/05M(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Statistics
Keywords: Decision trees, Logistic regression, Neural networks
Publication Number: 1527548
ISBN: 9781303795879
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