Book description
A revised edition that explores random numbers, probability, and statistical inference at an introductory mathematical level
Written in an engaging and entertaining manner, the revised and updated second edition of Probably Not continues to offer an informative guide to probability and prediction. The expanded second edition contains problem and solution sets. In addition, the book’s illustrative examples reveal how we are living in a statistical world, what we can expect, what we really know based upon the information at hand and explains when we only think we know something.
The author introduces the principles of probability and explains probability distribution functions. The book covers combined and conditional probabilities and contains a new section on Bayes Theorem and Bayesian Statistics, which features some simple examples including the Presecutor’s Paradox, and Bayesian vs. Frequentist thinking about statistics. New to this edition is a chapter on Benford’s Law that explores measuring the compliance and financial fraud detection using Benford’s Law. This book:
 Contains relevant mathematics and examples that demonstrate how to use the concepts presented
 Features a new chapter on Benford’s Law that explains why we find Benford’s law upheld in so many, but not all, natural situations
 Presents updated Life insurance tables
 Contains updates on the Gantt Chart example that further develops the discussion of random events
 Offers a companion site featuring solutions to the problem sets within the book
Written for mathematics and statistics students and professionals, the updated edition of Probably Not: Future Prediction Using Probability and Statistical Inference, Second Edition combines the mathematics of probability with realworld examples.
LAWRENCE N. DWORSKY, PhD, is a retired Vice President of the Technical Staff and Director of Motorola’s Components Research Laboratory in Schaumburg, Illinois, USA. He is the author of Introduction to Numerical Electrostatics Using MATLAB from Wiley.
Table of contents
 Cover
 Acknowledgments
 About the Companion Website
 Introduction

1 An Introduction to Probability
 Predicting the Future
 Rule Making
 Random Events and Probability
 The Lottery {Very Improbable Events and Very Large Data Sets}
 Coin Flipping {Fair Games, Looking Backward for Insight}
 The Coin Flip Strategy That Can't Lose
 The Prize Behind the Door {Looking Backward for Insight, Again}
 The Checker Board {Dealing With Only Part of the Data Set}
 Comments
 Problems
 2 Probability Distribution Functionsand Some Math Basics
 3 Building a Bell
 4 Random Walks
 5 Life Insurance
 6 The Binomial Theorem
 7 Pseudorandom Numbers and Monte Carlo Simulations
 8 Some Gambling Games in Detail
 9 Scheduling and Waiting
 10 Combined and Conditional Probabilities
 11 Bayesian Statistics
 12 Estimation Problems
 13 Two Paradoxes
 14 Benford's Law
 15 Networks, Infectious Diseases, and Chain Letters

16 Introduction to Frequentist Statistical Inference
 Introduction
 Sampling
 Sample Distributions and Standard Deviations
 Estimating Population Average from a Sample
 The Student‐T Distribution
 Did a Sample Come from a Given Population?
 A Little Reconciliation
 Correlation and Causality
 Correlation Coefficient
 Regression Lines
 Regression to the Mean
 Problems
 17 Statistical Mechanics and Thermodynamics
 18 Chaos and Quanta
 Appendix
 Index
 End User License Agreement
Product information
 Title: Probably Not, 2nd Edition
 Author(s):
 Release date: September 2019
 Publisher(s): Wiley
 ISBN: 9781119518105
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