
Book DetailsPure and Applied Undergraduate TextsVolume: 13; 2011; 615 ppMSC: Primary 62; Secondary 60;
Now available in Second Edition: AMSTEXT/28
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.ReadershipUndergraduate students interested in statistics.

Table of Contents

Cover

Title page

Contents

Preface

What is statistics?

Summarizing data

Probability and random variables

Continuous distributions

Parameter estimation and testing

Likelihoodbased statistics

Introduction to linear models

More linear models

A brief introduction to R

Some mathematical preliminaries

Geometry and linear algebra review

Review of Chapters 1–4

Hints, answers, and solutions to selected exercises

Bibliography

Index to R functions, packages, and data sets

Index

Back Cover


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Now available in Second Edition: AMSTEXT/28
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.
Undergraduate students interested in statistics.

Cover

Title page

Contents

Preface

What is statistics?

Summarizing data

Probability and random variables

Continuous distributions

Parameter estimation and testing

Likelihoodbased statistics

Introduction to linear models

More linear models

A brief introduction to R

Some mathematical preliminaries

Geometry and linear algebra review

Review of Chapters 1–4

Hints, answers, and solutions to selected exercises

Bibliography

Index to R functions, packages, and data sets

Index

Back Cover