Probability and Mathematical Statistics
Theory, Applications, and Practice in R
Will be published approx. on 30. July 2019
Book
Hardback
707 pages
978-1-61197-577-2 (ISBN)
Description
This book develops the theory of probability and mathematical statistics with the goal of analyzing real-world data. Throughout the text, the R package is used to compute probabilities, check analytically computed answers, simulate probability distributions, illustrate answers with appropriate graphics, and help students develop intuition surrounding probability and statistics. Examples, demonstrations, and exercises in the R programming language serve to reinforce ideas and facilitate understanding and confidence.
The book's Chapter Highlights provide a summary of key concepts, while the examples utilizing R within the chapters are instructive and practical. Exercises that focus on real-world applications without sacrificing mathematical rigor are included, along with more than 200 figures that help clarify both concepts and applications. In addition, the book features two helpful appendices: annotated solutions to 700 exercises and a Review of Useful Math.
The book's Chapter Highlights provide a summary of key concepts, while the examples utilizing R within the chapters are instructive and practical. Exercises that focus on real-world applications without sacrificing mathematical rigor are included, along with more than 200 figures that help clarify both concepts and applications. In addition, the book features two helpful appendices: annotated solutions to 700 exercises and a Review of Useful Math.
More details
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Weight
1650 gr
ISBN-13
978-1-61197-577-2 (9781611975772)
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Schweitzer Classification
Person
Mary C. Meyer is a statistics professor at Colorado State University, where her main area of research is estimation and inference in statistical models with inequality constraints. This includes nonparametric function estimation using constrained regression splines, density and hazard function estimation with shape constraints, and models with order restrictions.