Basic Concepts of Probability and Statistics in the Law

Name: Basic Concepts of Probability and Statistics in the Law
Price: 15.16 USD
Availability: InStock
ISBN: 9780387875002

ISBN-10: 038787500X

ISBN-13: 9780387875002

Edition: 2009

Authors: Michael O. Finkelstein

List price: $54.99

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This text provides a basic introduction to concepts of probability and statistics specifically for lawyers.

Book details

List price: $54.99
Copyright year: 2009
Publisher: Springer New York
Publication date: 6/11/2009
Binding: Paperback
Pages: 174
Size: 6.10" wide x 9.25" long x 0.50" tall
Weight: 1.320
Language: English




Probability


Classical and Legal Probability


Bayes's Theorem


Screening Tests


Debate over Bayesian analysis



Descriptive Tools


Measures of Central Location


Mean


Median


Mode


Variants of the Mean


Measures of Dispersion


Variance and Standard Deviation


Variance of Sample Sums and the Sample Mean


Correlation


Measuring the Disparity Between Two Proportions



Compound Events


The Addition Rule


The Product Rule



Significance


The Concept of Significance


Rejecting the Null Hypothesis


The Two-or-Three Standard Error Rule


Statistical and Legal Significance


Factors That Determine Significance


Nonsignificant Data



Random Variables and Their Distributions


Expectation, Variance, and Correlation


The Binomial Distribution


The Hypergeometric Distribution


The Normal Distribution


The Poisson Distribution


Student's t-Distribution


The Geometric and Exponential Distributions



Confidence



Power



Sampling


What is Sampling?


Simple Random Sampling


The Problem of Bias


More Complex Designs


Small Samples



Epidemiology


Cohort Studies


Case-Control Studies


Biases and Confounding


Association vs. Causation



Combining the Evidence


Aggregated and Disaggregated Data


Meta-analysis



Regression Models


Four Models


Pay Discrimination in an Agricultural Extension Service


Nitrogen Oxide Emission Reduction


Daily Driving Limits for Truckers


Bloc Voting


Estimating the Model


Measuring Indeterminacy in Models


Inherent Variability


Sampling Error


Confidence and Prediction Intervals in Relation to the Regression Estimate



Regression Models - Further Considerations


Choice of Explanatory Variables


Proxy Variables


Tainted Variables


Aggregation Issues


Forms of Models


Forms for Variables


Logarithms


Quadratic Terms


Interactive Variables


Measuring Uncertainty in Models


Assumptions of the Model


First Assumption: The Errors Have Zero Mean


Second Assumption: The Errors Are Independent


Third Assumption: The Errors Have Constant Variance


Fourth Assumption: The Errors Are Normally Distributed


Validating the Model


Logistic Regression


Table of Cases


Index