An Introduction to Probability and Statistics

Vijay K Rohatgi

ISBN 10: 111879964X / ISBN 13: 9781118799642

Verlag: Wiley Sep 2015, 2015

Beschreibung

Neuware - A well-balanced introduction to probability theory and mathematical statisticsFeaturing updated material, An Introduction to Probability and Statistics, Third Edition remains a solid overview to probability theory and mathematical statistics. Divided intothree parts, the Third Edition begins by presenting the fundamentals and foundationsof probability. The second part addresses statistical inference, and the remainingchapters focus on special topics.An Introduction to Probability and Statistics, Third Edition includes:\* A new section on regression analysis to include multiple regression, logistic regression, and Poisson regression\* A reorganized chapter on large sample theory to emphasize the growing role of asymptotic statistics\* Additional topical coverage on bootstrapping, estimation procedures, and resampling\* Discussions on invariance, ancillary statistics, conjugate prior distributions, and invariant confidence intervals\* Over 550 problems and answers to most problems, as well as 350 worked out examples and 200 remarks\* Numerous figures to further illustrate examples and proofs throughoutAn Introduction to Probability and Statistics, Third Edition is an ideal reference and resource for scientists and engineers in the fields of statistics, mathematics, physics, industrial management, and engineering. The book is also an excellent text for upper-undergraduate and graduate-level students majoring in probability and statistics.

Bestandsnummer des Verkäufers 9781118799642

Bibliografische Details

• Titel: An Introduction to Probability and Statistics
• Verlag: Wiley Sep 2015
• Erscheinungsjahr: 2015
• Sprache: Englisch
• ISBN-10: 111879964X
• ISBN-13: 9781118799642
• Einband: Buch
• Auflage: 3. Auflage
• Zustand: Neu
• Abmessungen: 240B x 161 H x 43 T Millimeter Breite: 240 mm, Höhe: 161 mm, Tiefe: 43 mm
• Gewicht: 1241 Gramm

Über diesen Titel

Inhaltsangabe

A well-balanced introduction to probability theory and mathematical statistics

Featuring updated material, An Introduction to Probability and Statistics, Third Edition remains a solid overview to probability theory and mathematical statistics. Divided intothree parts, the Third Edition begins by presenting the fundamentals and foundationsof probability. The second part addresses statistical inference, and the remainingchapters focus on special topics.

An Introduction to Probability and Statistics, Third Edition includes:

• A new section on regression analysis to include multiple regression, logistic regression, and Poisson regression
• A reorganized chapter on large sample theory to emphasize the growing role of asymptotic statistics
• Additional topical coverage on bootstrapping, estimation procedures, and resampling
• Discussions on invariance, ancillary statistics, conjugate prior distributions, and invariant confidence intervals
• Over 550 problems and answers to most problems, as well as 350 worked out examples and 200 remarks
• Numerous figures to further illustrate examples and proofs throughout

An Introduction to Probability and Statistics, Third Edition is an ideal reference and resource for scientists and engineers in the fields of statistics, mathematics, physics, industrial management, and engineering. The book is also an excellent text for upper-undergraduate and graduate-level students majoring in probability and statistics.

Über die Autorin bzw. den Autor

Vijay K. Rohatgi, PhD, is Professor Emeritus in the Department of Mathematics and Statistics at Bowling Green State University.  An Investment Research Consultant for PRI Investments, he is also the author of several books and over 100 research papers. 

A. K. Md. Ehsanes Saleh, PhD, is Distinguished Research Professor in the School of Mathematics and Statistics at Carleton University. Dr. Saleh is the author of more than 200 journal articles, and his research interests include nonparametric statistics, order statistics, and robust estimation.