Statistics for Lawyers

• Κύριοι συγγραφείς: Finkelstein, Michael O. (Συγγραφέας), Levin, Bruce (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: New York, NY : Springer New York, 2001.
• Έκδοση: Second Edition.
• Σειρά: Statistics for Social Science and Behavorial Sciences
• Θέματα: Statistics.; Commercial law.; Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law.; Commercial Law.

 Statistics for Lawyers is designed to introduce law students, law teachers, practitioners, and judges to the basic ideas of mathematical probability and statistics as they have been applied in the law. The book consists of sections of exposition followed by real-world cases and case studies in which statistical data have played a role. The reader is asked to apply the theory to the facts, to calculate results (a hand calculator is sufficient), and to explore legal issues raised by quantitative findings. The authors'calculations and comments are given in the back of the book. The cases and case studies reflect a broad variety of legal subjects, including antidiscrimination, mass torts, taxation, school finance, identification evidence, preventive detention, handwriting disputes, voting, environmental protection, antitrust, and the death penalty. The first edition of Statistics for Lawyers, which appeared in 1990, has been used in law, statistics, and social science courses. In 1991 it was selected by the University of Michigan Law Review as one of the important law books of the year. This second edition includes many new problems reflecting current developments in the law, including a new chapter on epidemiology. 

 Descriptive Statistics -- How to Count -- Elements of Probability -- Some Probability Distributions -- Statistical Inference for Two Proportions -- Comparing Multiple Proportions -- Comparing Means -- Combining Evidence Across Independent Strata -- Sampling Issues -- Epidemiology -- Survival Analysis -- Nonparametric Methods -- Regression Models -- More Complex Regression Models.