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

**Publisher:**Cengage Learning

**ISBN:**9780495385080

**Category:**Matematisk statistik

**Page:**912

**View:**7576

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# Search Results for: mathematical-statistics-with-applications

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

**Publisher:** Cengage Learning

**ISBN:** 9780495385080

**Category:** Matematisk statistik

**Page:** 912

**View:** 7576

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**: Kandethody M. Ramachandran,Chris P. Tsokos

**Publisher:** Elsevier

**ISBN:** 012417132X

**Category:** Mathematics

**Page:** 826

**View:** 4276

Mathematical Statistics with Applications in R, Second Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The book covers many modern statistical computational and simulation concepts that are not covered in other texts, such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. By combining the discussion on the theory of statistics with a wealth of real-world applications, the book helps students to approach statistical problem solving in a logical manner. This book provides a step-by-step procedure to solve real problems, making the topic more accessible. It includes goodness of fit methods to identify the probability distribution that characterizes the probabilistic behavior or a given set of data. Exercises as well as practical, real-world chapter projects are included, and each chapter has an optional section on using Minitab, SPSS and SAS commands. The text also boasts a wide array of coverage of ANOVA, nonparametric, MCMC, Bayesian and empirical methods; solutions to selected problems; data sets; and an image bank for students. Advanced undergraduate and graduate students taking a one or two semester mathematical statistics course will find this book extremely useful in their studies. Step-by-step procedure to solve real problems, making the topic more accessible Exercises blend theory and modern applications Practical, real-world chapter projects Provides an optional section in each chapter on using Minitab, SPSS and SAS commands Wide array of coverage of ANOVA, Nonparametric, MCMC, Bayesian and empirical methods

**Author**: Jay L. Devore,Kenneth N. Berk

**Publisher:** Springer Science & Business Media

**ISBN:** 146140391X

**Category:** Mathematics

**Page:** 845

**View:** 6780

Modern Mathematical Statistics with Applications, Second Edition strikes a balance between mathematical foundations and statistical practice. In keeping with the recommendation that every math student should study statistics and probability with an emphasis on data analysis, accomplished authors Jay Devore and Kenneth Berk make statistical concepts and methods clear and relevant through careful explanations and a broad range of applications involving real data. The main focus of the book is on presenting and illustrating methods of inferential statistics that are useful in research. It begins with a chapter on descriptive statistics that immediately exposes the reader to real data. The next six chapters develop the probability material that bridges the gap between descriptive and inferential statistics. Point estimation, inferences based on statistical intervals, and hypothesis testing are then introduced in the next three chapters. The remainder of the book explores the use of this methodology in a variety of more complex settings. This edition includes a plethora of new exercises, a number of which are similar to what would be encountered on the actuarial exams that cover probability and statistics. Representative applications include investigating whether the average tip percentage in a particular restaurant exceeds the standard 15%, considering whether the flavor and aroma of Champagne are affected by bottle temperature or type of pour, modeling the relationship between college graduation rate and average SAT score, and assessing the likelihood of O-ring failure in space shuttle launches as related to launch temperature.

**Author**: Laura M. Chihara,Tim C. Hesterberg

**Publisher:** John Wiley & Sons

**ISBN:** 1119416531

**Category:** Mathematics

**Page:** 560

**View:** 9215

This thoroughly updated second edition combines the latest software applications with the benefits of modern resampling techniques Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. The second edition of Mathematical Statistics with Resampling and R combines modern resampling techniques and mathematical statistics. This book has been classroom-tested to ensure an accessible presentation, uses the powerful and flexible computer language R for data analysis and explores the benefits of modern resampling techniques. This book offers an introduction to permutation tests and bootstrap methods that can serve to motivate classical inference methods. The book strikes a balance between theory, computing, and applications, and the new edition explores additional topics including consulting, paired t test, ANOVA and Google Interview Questions. Throughout the book, new and updated case studies are included representing a diverse range of subjects such as flight delays, birth weights of babies, and telephone company repair times. These illustrate the relevance of the real-world applications of the material. This new edition: • Puts the focus on statistical consulting that emphasizes giving a client an understanding of data and goes beyond typical expectations • Presents new material on topics such as the paired t test, Fisher's Exact Test and the EM algorithm • Offers a new section on "Google Interview Questions" that illustrates statistical thinking • Provides a new chapter on ANOVA • Contains more exercises and updated case studies, data sets, and R code Written for undergraduate students in a mathematical statistics course as well as practitioners and researchers, the second edition of Mathematical Statistics with Resampling and R presents a revised and updated guide for applying the most current resampling techniques to mathematical statistics.

**Author**: Irwin Miller,Marylees Miller

**Publisher:** Pearson

**ISBN:** 0134291670

**Category:** Mathematics

**Page:** 544

**View:** 8288

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. John E. Freund's Mathematical Statistics with Applications , Eighth Edition, provides a calculus-based introduction to the theory and application of statistics, based on comprehensive coverage that reflects the latest in statistical thinking, the teaching of statistics, and current practices.

**Author**: N.A

**Publisher:** Academic Press

**ISBN:** 0123854954

**Category:**

**Page:** N.A

**View:** 2809

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

**Publisher:** Duxbury Press

**ISBN:** 9780495385066

**Category:** Mathematics

**Page:** 176

**View:** 6991

Prepare for exams and succeed in your mathematics course with this comprehensive solutions manual! Featuring worked out-solutions to the problems in MATHEMATICAL STATISTICS WITH APPLICATIONS, 7th Edition, this manual shows you how to approach and solve problems using the same step-by-step explanations found in your textbook examples.

**Author**: Asha Seth Kapadia,Wenyaw Chan,Lemuel A. Moyé

**Publisher:** CRC Press

**ISBN:** 135199204X

**Category:** Mathematics

**Page:** 648

**View:** 6108

Mathematical statistics typically represents one of the most difficult challenges in statistics, particularly for those with more applied, rather than mathematical, interests and backgrounds. Most textbooks on the subject provide little or no review of the advanced calculus topics upon which much of mathematical statistics relies and furthermore contain material that is wholly theoretical, thus presenting even greater challenges to those interested in applying advanced statistics to a specific area. Mathematical Statistics with Applications presents the background concepts and builds the technical sophistication needed to move on to more advanced studies in multivariate analysis, decision theory, stochastic processes, or computational statistics. Applications embedded within theoretical discussions clearly demonstrate the utility of the theory in a useful and relevant field of application and allow readers to avoid sudden exposure to purely theoretical materials. With its clear explanations and more than usual emphasis on applications and computation, this text reaches out to the many students and professionals more interested in the practical use of statistics to enrich their work in areas such as communications, computer science, economics, astronomy, and public health.

**Author**: M. M. Rao

**Publisher:** Elsevier

**ISBN:** 0080925367

**Category:** Mathematics

**Page:** 495

**View:** 6945

The material in this book is designed for a standard graduate course on probability theory, including some important applications. It was prepared from the sets of lecture notes for a course that I have taught several times over the past 20 years. The present version reflects the reactions of my audiences as well as some of the textbooks that I used.

**Author**: Brian Caffo,Dennis D. Wackerly,Galin Jones

**Publisher:** Thomson Learning

**ISBN:** 9780534382360

**Category:** Mathematics

**Page:** 186

**View:** 9864

Worked-out solutions to odd-numbered exercises.
*An Introduction with Applications in Data Science*

**Author**: Roman Vershynin

**Publisher:** Cambridge University Press

**ISBN:** 1108415199

**Category:** Business & Economics

**Page:** 296

**View:** 6537

An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.

**Author**: Shen Liu,James Mcgree,Zongyuan Ge,Yang Xie

**Publisher:** Academic Press

**ISBN:** 0081006519

**Category:** Mathematics

**Page:** 206

**View:** 8328

Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration. Computational and Statistical Methods for Analysing Big Data with Applications starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data. Advanced computational and statistical methodologies for analysing big data are developed Experimental design methodologies are described and implemented to make the analysis of big data more computationally tractable Case studies are discussed to demonstrate the implementation of the developed methods Five high-impact areas of application are studied: computer vision, geosciences, commerce, healthcare and transportation Computing code/programs are provided where appropriate

**Author**: V. V. Nalimov

**Publisher:** Elsevier

**ISBN:** 148318479X

**Category:** Science

**Page:** 304

**View:** 8448

The Application of Mathematical Statistics to Chemical Analysis presents the methods of mathematical statistics as applied to problems connected with chemical analysis. This book is divided into nine chapters that particularly consider the principal theorems of mathematical statistics that are explained with examples taken from researchers associated with chemical analysis in laboratory work. This text deals first with the problems of mathematical statistics as a means to summarize information in chemical analysis. The next chapters examine the classification of errors, random variables and their characteristics, and the normal distribution in mathematical statistics. These topics are followed by surveys of the application of Poisson's and binomial distribution in radiochemical analysis; the estimation of chemical analytic results; and the principles and application of determination of experimental variance. The last chapters explore the determination of statistical parameters of linear relations and some working methods associated with the statistical design of an experiment. This book will be of great value to analytical chemists and mathematical statisticians.
*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:** 5081

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.

**Author**: Wilfried Grossmann

**Publisher:** Springer Science & Business Media

**ISBN:** 9789027725479

**Category:** Mathematics

**Page:** 456

**View:** 1650

Proceedings of the 5th Pannonian Symposium, Visegrad, Hungary, May 20-24, 1985

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

**Publisher:** Pearson

**ISBN:** 0134114248

**Category:** Mathematics

**Page:** 768

**View:** 5171

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**: Randall Pruim

**Publisher:** American Mathematical Soc.

**ISBN:** 1470428482

**Category:** Mathematical statistics

**Page:** 820

**View:** 5622

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.
*Mathematical Statistics Through Applications*

**Author**: Deborah Nolan,Terry P. Speed

**Publisher:** Springer Science & Business Media

**ISBN:** 0387227431

**Category:** Mathematics

**Page:** 283

**View:** 5493

Integrating the theory and practice of statistics through a series of case studies, each lab introduces a problem, provides some scientific background, suggests investigations for the data, and provides a summary of the theory used in each case. Aimed at upper-division students.

**Author**: Judy Hails

**Publisher:** Cengage Learning

**ISBN:** 1285062868

**Category:** Social Science

**Page:** 640

**View:** 4811

With an emphasis on real-world applications, CRIMINAL EVIDENCE, Eighth Edition, provides readers with comprehensive, up-to-date coverage of common evidentiary topics in a brief, affordable format. This book thoroughly explores constitutional issues essential to the collection and seizure of admissible evidence and legal interrogation, carefully outlining concepts and processes applicable to every state and pointing out where great interstate variation exists or specific state codes may have a strong impact. Current trends and topics discussed include terrorism and homeland security, scientific evidence, Federal Rules of Evidence, hearsay, and the appellate system. Available with InfoTrac Student Collections http://gocengage.com/infotrac. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
*Probability and Mathematical Statistics: A Series of Monographs and Textbooks*

**Author**: Michael R. Anderberg

**Publisher:** Academic Press

**ISBN:** 1483191397

**Category:** Mathematics

**Page:** 376

**View:** 3992

Cluster Analysis for Applications deals with methods and various applications of cluster analysis. Topics covered range from variables and scales to measures of association among variables and among data units. Conceptual problems in cluster analysis are discussed, along with hierarchical and non-hierarchical clustering methods. The necessary elements of data analysis, statistics, cluster analysis, and computer implementation are integrated vertically to cover the complete path from raw data to a finished analysis. Comprised of 10 chapters, this book begins with an introduction to the subject of cluster analysis and its uses as well as category sorting problems and the need for cluster analysis algorithms. The next three chapters give a detailed account of variables and association measures, with emphasis on strategies for dealing with problems containing variables of mixed types. Subsequent chapters focus on the central techniques of cluster analysis with particular reference to computational considerations; interpretation of clustering results; and techniques and strategies for making the most effective use of cluster analysis. The final chapter suggests an approach for the evaluation of alternative clustering methods. The presentation is capped with a complete set of implementing computer programs listed in the Appendices to make the use of cluster analysis as painless and free of mechanical error as is possible. This monograph is intended for students and workers who have encountered the notion of cluster analysis.

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