Probably not : future prediction using probability and statistical inference / Lawrence N. Dworsky.

Format:

Book

Author/Creator:

Dworsky, Lawrence N., 1943- author.

Language:

English

Subjects (All):

Prediction theory.

Probabilities--Problems, exercises, etc.

Probabilities.

Mathematical statistics--Problems, exercises, etc.

Mathematical statistics.

Physical Description:

1 online resource (350 pages)

Edition:

Second edition.

Place of Publication:

Hoboken, New Jersey : Wiley, 2019.

System Details:

text file

Summary:

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 real-world 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.

Contents:

Introduction

An introduction to probability

Probability distribution functions and some math basics

Building a bell

Random walks

Life insurance

The binomial theorem

Pseudorandom numbers and Monte-Carlo simulations

Some gambling games in detail

Scheduling and waiting

Combined and conditional probabilities

Bayesian statistics

Estimation problems

Paradoxes

Benford's law

Networks, infectious diseases and chain letters

Introduction to frequentist statistical inference

Statistical mechanics and thermodynamics

Chaos and quanta

Appendix.

Notes:

Includes index.

Includes bibliographical references and index.

Description based on print version record.

ISBN:

1-119-51814-8

1-119-51813-X

OCLC:

1099539195