ISBN: 9781119518105 - probably not: future prediction using probability and statistical inference (28 resultados)

Paperback. Condición: new. Paperback. 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 presentedFeatures a new chapter on Benfords Law that explains why we find Benfords law upheld in so many, but not all, natural situationsPresents updated Life insurance tablesContains updates on the Gantt Chart example that further develops the discussion of random eventsOffers 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. An introduction to probability and statistics at an introductory level. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Paperback. Condición: New. 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 presentedFeatures a new chapter on Benford's Law that explains why we find Benford's law upheld in so many, but not all, natural situationsPresents updated Life insurance tablesContains updates on the Gantt Chart example that further develops the discussion of random eventsOffers 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.…

Condición: As New. Unread book in perfect condition.

Paperback. Condición: new. Paperback. 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 presentedFeatures a new chapter on Benfords Law that explains why we find Benfords law upheld in so many, but not all, natural situationsPresents updated Life insurance tablesContains updates on the Gantt Chart example that further develops the discussion of random eventsOffers 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. An introduction to probability and statistics at an introductory level. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

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Condición: New. 2019. 2nd Edition. Paperback. . . . . .

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Paperback. Condición: new. Paperback. 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 presentedFeatures a new chapter on Benfords Law that explains why we find Benfords law upheld in so many, but not all, natural situationsPresents updated Life insurance tablesContains updates on the Gantt Chart example that further develops the discussion of random eventsOffers 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. An introduction to probability and statistics at an introductory level. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Condición: New. 2019. 2nd Edition. Paperback. . . . . . Books ship from the US and Ireland.

Paperback. Condición: Brand New. 2nd edition. 333 pages. 9.25x6.25x0.50 inches. In Stock.

Condición: New. 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(r) from Wiley.

Condición: Gut. Zustand: Gut | Seiten: 352 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Condición: Sehr gut. Zustand: Sehr gut | Seiten: 352 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Taschenbuch. Condición: Neu. Neuware - 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.…

Paperback. Condición: New. 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 presentedFeatures a new chapter on Benford's Law that explains why we find Benford's law upheld in so many, but not all, natural situationsPresents updated Life insurance tablesContains updates on the Gantt Chart example that further develops the discussion of random eventsOffers 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.…

Condición: New. Brand New.

Softcover. Condición: gut. 2019. Probably Not In englischer Sprache. pages.

Paperback. Condición: Brand New. 2nd edition. 333 pages. 9.25x6.25x0.50 inches. In Stock. This item is printed on demand.

Condición: New. PRINT ON DEMAND.