Foundations and applications of statistics : an introduction using R
 Responsibility
 Randall Pruim.
 Edition
 Second edition.
 Publication
 Providence, Rhode Island : American Mathematical Society, [2018]
 Physical description
 xx, 820 pages ; 27 cm.
 Series
 Pure and applied undergraduate texts ; 28.
Access
Available online
Science Library (Li and Ma)
Stacks
Call number  Status 

QA276.45 .R3 P78 2018  Unknown 
More options
Creators/Contributors
 Author/Creator
 Pruim, Randall J., author.
Contents/Summary
 Bibliography
 Includes bibliographical references and index.
 Contents

 Data Probability and random variables Continuous distributions Parameter estimation and testing Likelihood Introduction to linear models More linear models A brief introduction to $\mathsf{R}$ Some mathematical preliminaries Geometry and linear algebra review Hints, answers, and solutions to selected exercises Bibliography Index to $\mathsf{R}$ functions, packages, and data sets Index.
 (source: Nielsen Book Data)9781470428488 20180514
 Publisher's Summary
 Foundations and Applications of Statistics simultaneously emphasizes both the foundational and the computational aspects of modern statistics. Engaging and accessible, this book is useful to undergraduate students with a wide range of backgrounds and career goals. The exposition immediately begins with statistics, presenting concepts and results from probability along the way. Hypothesis testing is introduced very early, and the motivation for several probability distributions comes from pvalue computations. Pruim develops the students' practical statistical reasoning through explicit examples and through numerical and graphical summaries of data that allow intuitive inferences before introducing the formal machinery. The topics have been selected to reflect the current practice in statistics, where computation is an indispensible tool. In this vein, the statistical computing environment $\mathsf{R}$ is used throughout the text and is integral to the exposition. Attention is paid to developing students' mathematical and computational skills as well as their statistical reasoning. Linear models, such as regression and ANOVA, are treated with explicit reference to the underlying linear algebra, which is motivated geometrically. Foundations and Applications of Statistics discusses both the mathematical theory underlying statistics and practical applications that make it a powerful tool across disciplines. The book contains ample material for a twosemester course in undergraduate probability and statistics. A onesemester course based on the book will cover hypothesis testing and confidence intervals for the most common situations. In the second edition, the $\mathsf{R}$ code has been updated throughout to take advantage of new $\mathsf{R}$ packages and to illustrate better coding style. New sections have been added covering bootstrap methods, multinomial and multivariate normal distributions, the delta method, numerical methods for Bayesian inference, and nonlinear least squares. Also, the use of matrix algebra has been expanded, but remains optional, providing instructors with more options regarding the amount of linear algebra required.
(source: Nielsen Book Data)9781470428488 20180514
Subjects
Bibliographic information
 Publication date
 2018
 Series
 Pure and applied undergraduate texts ; 28
 ISBN
 9781470428488 hardcover
 1470428482 hardcover