Author: Richard J. Larsen
Release Date: 2017-10-24
This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. For courses in Mathematical Statistics Introducing the principles of statistics and data modeling Introduction to Mathematical Statistics and Its Applications , 6th Edition is a high-level calculus student’s first exposure to mathematical statistics. This book provides students who have already taken three or more semesters of calculus with the background to apply statistical principles. Meaty enough to guide a two-semester course, the book touches on both statistics and experimental design, which teaches students various ways to analyze data. It gives computational-minded students a necessary and realistic exposure to identifying data models.
Author: Richard J. Larsen
Publisher: Pearson College Division
Release Date: 2012
Noted for its integration of real-world data and case studies, this text offers sound coverage of the theoretical aspects of mathematical statistics. The authors demonstrate how and when to use statistical methods, while reinforcing the calculus that students have mastered in previous courses. Throughout the Fifth Edition, the authors have added and updated examples and case studies, while also refining existing features that show a clear path from theory to practice.
Author: Richard J. Larsen
Publisher: Pearson Educacion
Release Date: 2012
Genre: Mathematical statistics
Offering comprehensive coverage of the theoretical aspects of mathematical statistics, this text demonstrates how and when to use statistical methods, while reinforcing the calculus that students will have mastered in previous courses.
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 p-value 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 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 two-semester course in undergraduate probability and statistics. A one-semester course based on the book will cover hypothesis testing and confidence intervals for the most common situations. In the second edition, the R code has been updated throughout to take advantage of new 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.
Author: Gareth James
Publisher: Springer Science & Business Media
Release Date: 2013-06-24
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
In their bestselling MATHEMATICAL STATISTICS WITH APPLICATIONS, premiere authors Dennis Wackerly, William Mendenhall, and Richard L. Scheaffer present a solid foundation in statistical theory while conveying the relevance and importance of the theory in solving practical problems in the real world. The authors' use of practical applications and excellent exercises helps students discover the nature of statistics and understand its essential role in scientific research. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
Author: Robert V. Hogg
Publisher: Pearson Higher Ed
Release Date: 2013-08-29
Introduction to Mathematical Statistics, Seventh Edition, provides students with a comprehensive introduction to mathematical statistics. Continuing its proven approach, the Seventh Edition has been updated with new examples, exercises, and content for an even stronger presentation of the material.
Practical Business Statistics, Sixth Edition, is a conceptual , realistic, and matter-of-fact approach to managerial statistics that carefully maintains, but does not overemphasize, mathematical correctness. The book offers a deep understanding of how to learn from data and how to deal with uncertainty while promoting the use of practical computer applications. This teaches present and future managers how to use and understand statistics without an overdose of technical detail, enabling them to better understand the concepts at hand and to interpret results. The text uses excellent examples with real world data relating to the functional areas within Business such as finance, accounting, and marketing. It is well written and designed to help students gain a solid understanding of fundamental statistical principles without bogging them down with excess mathematical details. This edition features many examples and problems that have been updated with more recent data sets, and continues to use the ever-changing Internet as a data source. Supplemental materials include companion website with datasets and software. Each chapter begins with an overview, showing why the subject is important to business, and ends with a comprehensive summary, with key words, questions, problems, database exercises, projects, and cases in most chapters. This text is written for the introductory business/management statistics course offered for undergraduate students or Quantitative Methods in Management/ Analytics for Managers at the MBA level. User-friendly, lively writing style Separate writing chapter aids instructors in teaching how to explain quantitative analysis Over 200 carefully-drawn charts and graphs show how to visualize data Data mining is a theme that appears in many chapters, often featuring a large database (included on the website) of characteristics of 20,000 potential donors to a worthy cause and the amount actually given in response to a mailing Many of the examples and problems in the sixth edition have been updated with more recent data sets, and the ever-changing Internet continues to be featured as a data source Each chapter begins with an overview, showing why the subject is important to business, and ends with a comprehensive summary, with key words, questions, problems, database exercises, projects, and cases in most chapters All details are technically accurate (Professor Siegel has a PhD in Statistics from Stanford University and has given presentations on exploratory data analysis with its creator) while the book concentrates on the understanding and use of statistics by managers Features that have worked well for students and instructors in the first five editions have been retained
Author: Richard J. Larsen
Publisher: South-Western Pub
Release Date: 1997
Genre: Business & Economics
Intended for the applied calculus-based, introductory statistics course for business and economics undergraduates and MBAs, this book presents business statistics as a problem-solving process, rather than as a means to achieve a numerical solution. Applied, data-driven, and copmuter-oriented, it emphasizes the principles of statistics as a foundation for application of statistical methodologies and real-life problems. Curve fitting and regression are introduced early, allowing students to use the computer from the start of the course. Statistical and spreadsheet software help students make the connections between probability models and actual data. The authors include sparing and careful use of calculus at critical points where a simple calculus reference or computation can totally eliminate the perplexing arbitrariness that is seen in 'cookbook' approaches.
Author: Will H. Moore
Publisher: Princeton University Press
Release Date: 2013-07-24
Genre: Political Science
Political science and sociology increasingly rely on mathematical modeling and sophisticated data analysis, and many graduate programs in these fields now require students to take a "math camp" or a semester-long or yearlong course to acquire the necessary skills. Available textbooks are written for mathematics or economics majors, and fail to convey to students of political science and sociology the reasons for learning often-abstract mathematical concepts. A Mathematics Course for Political and Social Research fills this gap, providing both a primer for math novices in the social sciences and a handy reference for seasoned researchers. The book begins with the fundamental building blocks of mathematics and basic algebra, then goes on to cover essential subjects such as calculus in one and more than one variable, including optimization, constrained optimization, and implicit functions; linear algebra, including Markov chains and eigenvectors; and probability. It describes the intermediate steps most other textbooks leave out, features numerous exercises throughout, and grounds all concepts by illustrating their use and importance in political science and sociology. Uniquely designed and ideal for students and researchers in political science and sociology Uses practical examples from political science and sociology Features "Why Do I Care?" sections that explain why concepts are useful Includes numerous exercises Complete online solutions manual (available only to professors, email david.siegel at duke.edu, subject line "Solution Set") Selected solutions available online to students