Probably Not - Future Prediction Using Probability and Statistical Inference

Informationen zum Autor 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. Klappentext A revised edition that explores random numbers, probability, and statistical inference at an introductory mathematical levelWritten 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 bookWritten 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. Zusammenfassung A revised edition that explores random numbers, probability, and statistical inference at an introductory mathematical levelWritten 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 bookWritten for mathematics and statistics students and profes...

List of contents

Acknowledgments
 
About The Companion Site
 
Introduction
 
1 An Introduction to Probability
 
Predicting The Future
 
Rule Making
 
Random Events and Probability
 
The Lottery
 
Coin Flipping
 
The Coin Flip Strategy That Can't Lose
 
The Prize Behind The Door
 
The Checker Board
 
Comments
 
Problems
 
2 Probability Distribution Functions and Some Math Basics
 
The Probability Distribution Function
 
Averages and Weighted Averages
 
Expected Values
 
The Basic Coin Flip Game
 
PDF Symmetry
 
Standard Deviation
 
Cumulative Distribution Function
 
The Confidence Interval
 
Final Points
 
Rehash and Histograms
 
Problems
 
3 Building A Bell
 
Problems
 
4 Random Walks
 
The One-Dimensional Random Walk
 
Some Subsequent Calculations
 
Diffusion
 
Problems
 
5 Life Insurance
 
Introduction
 
Life Insurance
 
Insurance As Gambling
 
Life Tables
 
Birth Rates and Population Stability
 
Life Tables, Again
 
Premiums
 
Social Security - Sooner Or Later?
 
Problems
 
6 The Binomial Theorem
 
Introduction
 
The Binomial Probability Formula
 
Permutations and Combinations
 
Large Number Approximations
 
The Poisson Distribution
 
Disease Clusters
 
Clusters
 
Problems
 
7 Pseudorandom Numbers and Monte -Carlo Simulations
 
Random Numbers and Simulations
 
Pseudo-Random Numbers
 
The Middle Square PRNG
 
The Linear Congruential PRNG
 
A Normal Distribution Generator
 
An Arbitrary Distribution Generator
 
Monte Carlo Simulations
 
A League of Our Own
 
Discussion
 
Notes
 
8 Some Gambling Games In Detail
 
The Basic Coin Flip Game
 
The "Ultimate Winning Strategy"
 
Parimutuel Betting
 
The Gantt Chart and A Hint of Another Approach
 
Problems
 
9 Scheduling and Waiting
 
Introduction
 
Scheduling Appointments In The Doctor's Office
 
Lunch with A Friend
 
Waiting for A Bus
 
Problems
 
10 Combined and Conditional Probabilities
 
Introduction
 
Functional Notation (Again)
 
Conditional Probability
 
Medical Test Results
 
The Shared Birthday Problem
 
Problems
 
11 Bayesian Statistics
 
Bayes Theorem
 
Multiple Possibilities
 
Will Monty Hall Ever Go Away?
 
Philosophy
 
The Prosecutor's Fallacy
 
Continuous Functions
 
Credible Intervals
 
Gantt Charts (Again)
 
Problems
 
12 Estimation Problems
 
The Number of Locomotives Problem
 
Number of Locomotives, Improved Estimate
 
Decision Making
 
The Light House Problem
 
The Likelihood Function
 
The Light House Problem II
 
13 Two Paradoxes
 
Introduction
 
Parrondo's Paradox
 
Another Parrondo Game
 
The Parrondo Ratchet
 
Simpson's Paradox
 
Problems
 
14 Benford's Law
 
Introduction
 
History
 
The 1/x Distribution
 
Goodness of Fit Measure
 
Smith's Analysis
 
Problems
 
15 Networks, Infectious Diseases and Chain Letters
 
Introduction
 
Degre

Product details

• Authors: Lawrence N Dworsky, Lawrence N. Dworsky, Lawrence N. (Motorola Dworsky, Ln Dworsky, Dworsky Lawrence N.
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Paperback / Softback
• Released: 31.10.2019
• EAN: 9781119518105
• ISBN: 978-1-119-51810-5
• No. of pages: 352
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; Statistik, Wahrscheinlichkeitsrechnung, Statistics, Angewandte Wahrscheinlichkeitsrechnung u. Statistik, Applied Probability & Statistics, Wahrscheinlichkeitsrechnung u. mathematische Statistik, Probability & Mathematical Statistics, Populärwissenschaftliche Statistik, Popular Interest Statistics