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 books 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 Presecutors Paradox, and Bayesian vs. Frequentist thinking about statistics. New to this edition is a chapter on Benfords Law that explores measuring the compliance and financial fraud detection using Benfords Law. This book:

Contains relevant mathematics and examples that demonstrate how to use the concepts presented
Features a new chapter on Benfords Law that explains why we find Benfords 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 real-world examples.

LAWRENCE N. DWORSKY, PhD, is a retired Vice President of the Technical Staff and Director of Motorolas Components Research Laboratory in Schaumburg, Illinois, USA. He is the author of Introduction to Numerical Electrostatics Using MATLAB from Wiley.