**Author**: Richard J. Larsen,Morris L. Marx

**Publisher:**Pearson

**ISBN:**0134114248

**Category:**Mathematics

**Page:**768

**View:**7665

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# Search Results for: an-introduction-to-mathematical-statistics-and-its-applications-6th-edition

**Author**: Richard J. Larsen,Morris L. Marx

**Publisher:** Pearson

**ISBN:** 0134114248

**Category:** Mathematics

**Page:** 768

**View:** 7665

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,Morris L. Marx

**Publisher:** Pearson College Division

**ISBN:** 9780321693945

**Category:** Mathematics

**Page:** 757

**View:** 6138

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,Morris L. Marx

**Publisher:** Pearson Educacion

**ISBN:** 9780321766564

**Category:** Mathematical statistics

**Page:** 757

**View:** 687

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.
*with Applications in R*

**Author**: Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani

**Publisher:** Springer Science & Business Media

**ISBN:** 1461471389

**Category:** Mathematics

**Page:** 426

**View:** 3153

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.
*Student solutions manual*

**Author**: Richard J. Larsen,Morris L. Marx

**Publisher:** N.A

**ISBN:** 9780130310156

**Category:** Mathematical statistics

**Page:** 111

**View:** 5703

**Author**: Robert J. Bond,William J. Keane

**Publisher:** Waveland Press

**ISBN:** 1478608056

**Category:** Mathematics

**Page:** 323

**View:** 6157

Bond and Keane explicate the elements of logical, mathematical argument to elucidate the meaning and importance of mathematical rigor. With definitions of concepts at their disposal, students learn the rules of logical inference, read and understand proofs of theorems, and write their own proofs all while becoming familiar with the grammar of mathematics and its style. In addition, they will develop an appreciation of the different methods of proof (contradiction, induction), the value of a proof, and the beauty of an elegant argument. The authors emphasize that mathematics is an ongoing, vibrant disciplineits long, fascinating history continually intersects with territory still uncharted and questions still in need of answers. The authors extensive background in teaching mathematics shines through in this balanced, explicit, and engaging text, designed as a primer for higher- level mathematics courses. They elegantly demonstrate process and application and recognize the byproducts of both the achievements and the missteps of past thinkers. Chapters 1-5 introduce the fundamentals of abstract mathematics and chapters 6-8 apply the ideas and techniques, placing the earlier material in a real context. Readers interest is continually piqued by the use of clear explanations, practical examples, discussion and discovery exercises, and historical comments.
*Understanding Why and How*

**Author**: F.M. Dekking,C. Kraaikamp,H.P. Lopuhaä,L.E. Meester

**Publisher:** Springer Science & Business Media

**ISBN:** 1846281687

**Category:** Mathematics

**Page:** 488

**View:** 4889

Suitable for self study Use real examples and real data sets that will be familiar to the audience Introduction to the bootstrap is included – this is a modern method missing in many other books
*An Introduction with Applications*

**Author**: Bernt Øksendal

**Publisher:** Springer Science & Business Media

**ISBN:** 3642143946

**Category:** Mathematics

**Page:** 379

**View:** 3188

**Author**: R. Lyman Ott,Micheal T. Longnecker

**Publisher:** Cengage Learning

**ISBN:** 1305465520

**Category:** Mathematics

**Page:** 1296

**View:** 4870

Ott and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Seventh Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a variety of disciplines who have little or no prior course work in statistics. The authors teach students to solve problems encountered in research projects, to make decisions based on data in general settings both within and beyond the university setting, and to become critical readers of statistical analyses in research papers and news reports. The first eleven chapters present material typically covered in an introductory statistics course, as well as case studies and examples that are often encountered in undergraduate capstone courses. The remaining chapters cover regression modeling and design of experiments. 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,Joeseph McKean,Allen T Craig

**Publisher:** Pearson Higher Ed

**ISBN:** 1292037628

**Category:** Mathematics

**Page:** 656

**View:** 7428

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.

**Author**: Lee J. Bain,Max Engelhardt

**Publisher:** Duxbury Press

**ISBN:** 9780534380205

**Category:** Mathematics

**Page:** 644

**View:** 4249

The Second Edition of INTRODUCTION TO PROBABILITY AND MATHEMATICAL STATISTICS focuses on developing the skills to build probability (stochastic) models. Lee J. Bain and Max Engelhardt focus on the mathematical development of the subject, with examples and exercises oriented toward applications.

**Author**: Dennis D. Wackerly,William Mendenhall,Richard L. Scheaffer

**Publisher:** Cengage Learning

**ISBN:** 9780495385080

**Category:** Matematisk statistik

**Page:** 912

**View:** 1610

The authors present the theory of statistics in the context of practical problem solving and real world applications. This practical approach helps you discover the nature of statistics and comprehend its essential role in scientific research.--

**Author**: Shelemyahu Zacks

**Publisher:** John Wiley & Sons

**ISBN:** 1118605837

**Category:** Mathematics

**Page:** 652

**View:** 8347

Provides the necessary skills to solve problems in mathematical statistics through theory, concrete examples, and exercises With a clear and detailed approach to the fundamentals of statistical theory, Examples and Problems in Mathematical Statistics uniquely bridges the gap between theory andapplication and presents numerous problem-solving examples that illustrate the relatednotations and proven results. Written by an established authority in probability and mathematical statistics, each chapter begins with a theoretical presentation to introduce both the topic and the important results in an effort to aid in overall comprehension. Examples are then provided, followed by problems, and finally, solutions to some of the earlier problems. In addition, Examples and Problems in Mathematical Statistics features: Over 160 practical and interesting real-world examples from a variety of fields including engineering, mathematics, and statistics to help readers become proficient in theoretical problem solving More than 430 unique exercises with select solutions Key statistical inference topics, such as probability theory, statistical distributions, sufficient statistics, information in samples, testing statistical hypotheses, statistical estimation, confidence and tolerance intervals, large sample theory, and Bayesian analysis Recommended for graduate-level courses in probability and statistical inference, Examples and Problems in Mathematical Statistics is also an ideal reference for applied statisticians and researchers.

**Author**: Douglas C. Montgomery,George C. Runger

**Publisher:** John Wiley & Sons

**ISBN:** 047088844X

**Category:** Technology & Engineering

**Page:** 398

**View:** 6835

Montgomery and Runger's bestselling engineering statistics text provides a practical approach oriented to engineering as well as chemical and physical sciences. By providing unique problem sets that reflect realistic situations, students learn how the material will be relevant in their careers. With a focus on how statistical tools are integrated into the engineering problem-solving process, all major aspects of engineering statistics are covered. Developed with sponsorship from the National Science Foundation, this text incorporates many insights from the authors' teaching experience along with feedback from numerous adopters of previous editions.
*Probability and Mathematical Statistics*

**Author**: L. H. Koopmans

**Publisher:** Academic Press

**ISBN:** 1483218546

**Category:** Mathematics

**Page:** 382

**View:** 5881

The Spectral Analysis of Time Series describes the techniques and theory of the frequency domain analysis of time series. The book discusses the physical processes and the basic features of models of time series. The central feature of all models is the existence of a spectrum by which the time series is decomposed into a linear combination of sines and cosines. The investigator can used Fourier decompositions or other kinds of spectrals in time series analysis. The text explains the Wiener theory of spectral analysis, the spectral representation for weakly stationary stochastic processes, and the real spectral representation. The book also discusses sampling, aliasing, discrete-time models, linear filters that have general properties with applications to continuous-time processes, and the applications of multivariate spectral models. The text describes finite parameter models, the distribution theory of spectral estimates with applications to statistical inference, as well as sampling properties of spectral estimates, experimental design, and spectral computations. The book is intended either as a textbook or for individual reading for one-semester or two-quarter course for students of time series analysis users. It is also suitable for mathematicians or professors of calculus, statistics, and advanced mathematics.

**Author**: Kenneth Rosen

**Publisher:** McGraw-Hill Higher Education

**ISBN:** 007741893X

**Category:** Education

**Page:** N.A

**View:** 3804

**Author**: David S. Moore,George P. McCabe,Bruce A. Craig

**Publisher:** WH Freeman

**ISBN:** 9781319153977

**Category:** Mathematics

**Page:** 814

**View:** 8410

Introduction to the Practice of Statistics is the classic textbook for teaching statistics. This textbook shows students how to produce and interpret data from real-world contexts, guiding them through the type of data gathering and analysis that working statisticians do every day. With this phenomenally successful approach developed by David Moore and George McCabe, statistics is more than just a collection of techniques and formulas. Instead, students develop a way of thinking about data with a focus on problem-solving that helps them understand concepts and master statistical reasoning. Part of the best-selling Moore family of statistics books, Introduction to the Practice of Statistics is designed for a two-semester ‘introduction to statistics’ course and offers a rigorous introduction to the subject. This textbook is available on LaunchPad, which combines an interactive ebook with multimedia content and assessment tools, including LearningCurve adaptive quizzing. See ‘Instructor Resources’ and ‘Student Resources’ for further information.

**Author**: Roxy Peck,Chris Olsen,Jay L. Devore

**Publisher:** Cengage Learning

**ISBN:** 1305445961

**Category:** Mathematics

**Page:** 844

**View:** 2267

Roxy Peck, Chris Olsen, and Jay Devore's new edition uses real data and attention-grabbing examples to introduce students to the study of statistics and data analysis. Traditional in structure yet modern in approach, this text guides students through an intuition-based learning process that stresses interpretation and communication of statistical information. Simple notation--including frequent substitution of words for symbols--helps students grasp concepts and cement their comprehension. Hands-on activities and interactive applets allow students to practice statistics firsthand. INTRODUCTION TO STATISTICS AND DATA ANALYSIS includes updated coverage of most major technologies, as well as expanded coverage of probability. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

**Author**: Michael J. Crawley

**Publisher:** John Wiley & Sons

**ISBN:** 1118448960

**Category:** Mathematics

**Page:** 1080

**View:** 865

Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: Features full colour text and extensive graphics throughout. Introduces a clear structure with numbered section headings to help readers locate information more efficiently. Looks at the evolution of R over the past five years. Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully revised and updated bibliography and reference section. Is supported by an accompanying website allowing examples from the text to be run by the user. Praise for the first edition: ‘…if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.’ (The American Statistician, August 2008) ‘The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book…’ (Professional Pensions, July 2007)

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