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

## Mathematical Statistics with Applications

**Author**: Dennis Wackerly,William Mendenhall,Richard L. Scheaffer**Publisher:**Cengage Learning**ISBN:**1111798788**Category:**Mathematics**Page:**944**View:**6298

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.

## Mathematical Statistics with Applications

**Author**: Dennis D. Wackerly,Richard L. Scheaffer**Publisher:**Brooks/Cole**ISBN:**N.A**Category:**Mathematics**Page:**853**View:**6332

This is the most widely used mathematical statistics text at the top 200 universities in the United States. Premiere authors Dennis Wackerly, William Mendenhall, and Richard L. Scheaffer present a solid undergraduate 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.

## Modern Mathematical Statistics with Applications

**Author**: Jay L. Devore,Kenneth N. Berk**Publisher:**Springer Science & Business Media**ISBN:**146140391X**Category:**Mathematics**Page:**845**View:**6993

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.

## Mathematical Statistics with Applications in R

**Author**: Kandethody M. Ramachandran,Chris P. Tsokos**Publisher:**Elsevier**ISBN:**012417132X**Category:**Mathematics**Page:**826**View:**595

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

## Student Solutions Manual, Mathematical Statistics with Applications

**Author**: N.A**Publisher:**Academic Press**ISBN:**0123854954**Category:****Page:**N.A**View:**1687

## Mathematical Statistics with Applications

**Author**: Dennis D. Wackerly,Richard L. Scheaffer**Publisher:**PWS Publishing Company**ISBN:**9780534209162**Category:**Mathematical statistics**Page:**798**View:**8995

This is the most widely used mathematical statistics book at the top 200 universities in the United States. 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 readers discover the nature of statistics and understand its essential role in scientific research.

## Mathematical statistics with applications

**Author**: William Mendenhall,Richard L. Scheaffer,Dennis D. Wackerly**Publisher:**Arden Shakespeare**ISBN:**N.A**Category:**Mathematics**Page:**750**View:**9142

This is the most widely used mathematical statistics text at the top 200 universities in the United States. Premiere authors Dennis Wackerly, William Mendenhall, and Richard L. Scheaffer present a solid undergraduate 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.

## Mathematical Statistics With Applications

**Author**: Asha Seth Kapadia,Wenyaw Chan,Lemuel A. Moyé**Publisher:**CRC Press**ISBN:**135199204X**Category:**Mathematics**Page:**648**View:**4983

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.

## Mathematical Statistics With Applications

**Author**: Charles D. Kincaid,Dennis Wackerly,William Mendenhall,Richard L. Scheaffer**Publisher:**Brooks/Cole**ISBN:**9780534382360**Category:**Mathematics**Page:**N.A**View:**7855

Worked-out solutions to odd-numbered exercises.

## Probability Theory and Mathematical Statistics with Applications

**Author**: Wilfried Grossmann,J. Mogyoródi,I. Vincze,Wolfgang Wertz**Publisher:**Springer Science & Business Media**ISBN:**9789027725479**Category:**Mathematics**Page:**458**View:**9330

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

## Wahrscheinlichkeit

**Author**: Alʹbert Nikolaevich Shiri︠a︡ev,Hans Jürgen Engelbert**Publisher:**N.A**ISBN:**N.A**Category:**Probabilities**Page:**592**View:**3138

## Student Solutions Manual, Mathematical Statistics with Applications

*Student Solutions Manual*

**Author**: K.M. Ramachandran,Chris P. Tsokos**Publisher:**Academic Press**ISBN:**0080964435**Category:**Science**Page:**148**View:**8322

Mathematical Statistics with Applications provides a calculus-based theoretical introduction to mathematical statistics while emphasizing interdisciplinary applications as well as exposure to modern statistical computational and simulation concepts that are not covered in other textbooks. Includes the Jackknife, Bootstrap methods, the EM algorithms and Markov chain Monte Carlo methods. Prior probability or statistics knowledge is not required. 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

## John E. Freund's Mathematical Statistics with Applications

**Author**: John E. Freund,Irwin Miller,Marylees Miller**Publisher:**N.A**ISBN:**9780131246461**Category:**Mathematical statistics**Page:**614**View:**7068

Suitable for a two-semester or three-quarter calculus-based course in Introduction to Mathematical Statistics, this title provides a calculus-based introduction to the theory and application of statistics. Emphasis has been placed on the use of computers in performing statistical calculations by including computer exercises.

## Adaptive Lösung partieller Differentialgleichungen

**Author**: Peter Deuflhard,Martin Weiser**Publisher:**Walter de Gruyter**ISBN:**3110218038**Category:**Mathematics**Page:**442**View:**1840

Thistextbook expands the standard work on numerical mathematics to include the numerics of partial differential equations. The volume is intended for students of mathematics as well as physicists, chemists and engineers who are confronted with finding efficient solutions for complex application problems.

## John E. Freund's Mathematical Statistics with Applications

**Author**: John E. Freund,Irwin Miller,Marylees Miller**Publisher:**Pearson College Division**ISBN:**9780131427068**Category:**Mathematics**Page:**614**View:**1804

'John E. Freund's Mathematical Statistics' is a calculus-based introduction to the mathematics of statistics. This edition deals in greater depth with some of the applications of the theory.

## John E. Freund's Mathematical Statistics with Applications (Classic Version)

**Author**: Irwin Miller,Marylees Miller**Publisher:**Pearson**ISBN:**9780134995373**Category:**Mathematical statistics**Page:**544**View:**4656

"This text is designed primarily for a two-semester or three-quarter calculus-based course in mathematical statistics."--

## Devore/Berk's Modern Mathematical Statistics With Applications

**Author**: Matthew A. Carlton,Kenneth N. Berk,Jay L. Devore**Publisher:**Duxbury Press**ISBN:**9780534404741**Category:**Mathematics**Page:**252**View:**989

The Student Solutions Manual provides worked-out solutions to the selected problems in the text.

## Mathematical Statistics with Resampling and R

**Author**: Laura M. Chihara,Tim C. Hesterberg**Publisher:**John Wiley & Sons**ISBN:**1118518950**Category:**Mathematics**Page:**432**View:**4496

This book bridges 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. This groundbreaking book shows how to apply modern resampling techniques to mathematical statistics. Extensively class-tested to ensure an accessible presentation, Mathematical Statistics with Resampling and R utilizes the powerful and flexible computer language R to underscore the significance and benefits of modern resampling techniques. The book begins by introducing permutation tests and bootstrap methods, motivating classical inference methods. Striking a balance between theory, computing, and applications, the authors explore additional topics such as: Exploratory data analysis Calculation of sampling distributions The Central Limit Theorem Monte Carlo sampling Maximum likelihood estimation and properties of estimators Confidence intervals and hypothesis tests Regression Bayesian methods Throughout the book, case studies on diverse subjects such as flight delays, birth weights of babies, and telephone company repair times illustrate the relevance of the real-world applications of the discussed material. Key definitions and theorems of important probability distributions are collected at the end of the book, and a related website is also available, featuring additional material including data sets, R scripts, and helpful teaching hints. Mathematical Statistics with Resampling and R is an excellent book for courses on mathematical statistics at the upper-undergraduate and graduate levels. It also serves as a valuable reference for applied statisticians working in the areas of business, economics, biostatistics, and public health who utilize resampling methods in their everyday work.