Probability and Mathematical Statistics by Mary C. Meyer - ISBN: 9781611975772
Hardcover
Develops the theory of probability and mathematical statistics with the goal of analysing real-world data. Throughout, the R package is used to compute probabilities, check analytically computed answers, simulate probability distributions, illustrate answers with appropriate graphics, and help stude…

Probability and Mathematical Statistics

Theory, Applications, and Practice in R

$252.65

  • Hardcover

    707 pages

  • Release Date

    29 July 2019

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Summary

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.

Book Details

ISBN-13:9781611975772
ISBN-10:1611975778
Author:Mary C. Meyer
Publisher:Society for Industrial & Applied Mathematics,U.S.
Imprint:Society for Industrial & Applied Mathematics,U.S.
Format:Hardcover
Number of Pages:707
Release Date:29 July 2019
Weight:1.65kg
About The Author

Mary C. Meyer

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.

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