Mathematical Statistics with Applications


Author: Dennis Wackerly,William Mendenhall,Richard L. Scheaffer
Publisher: Cengage Learning
ISBN: 1111798788
Category: Mathematics
Page: 944
View: 2966

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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: 6694

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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 in R


Author: Kandethody M. Ramachandran,Chris P. Tsokos
Publisher: Elsevier
ISBN: 012417132X
Category: Mathematics
Page: 826
View: 8593

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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

Mathematical Statistics with Applications


Author: Dennis D. Wackerly,Richard L. Scheaffer
Publisher: PWS Publishing Company
ISBN: 9780534209162
Category: Mathematical statistics
Page: 798
View: 6452

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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.

Modern Mathematical Statistics with Applications


Author: Jay L. Devore,Kenneth N. Berk
Publisher: Springer Science & Business Media
ISBN: 146140391X
Category: Mathematics
Page: 845
View: 3175

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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


Author: William Mendenhall,Richard L. Scheaffer,Dennis D. Wackerly
Publisher: Arden Shakespeare
ISBN: N.A
Category: Mathematics
Page: 750
View: 8436

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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: 792

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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.

Wahrscheinlichkeit


Author: Alʹbert Nikolaevich Shiri︠a︡ev,Hans Jürgen Engelbert
Publisher: N.A
ISBN: N.A
Category: Probabilities
Page: 592
View: 3111

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Adaptive Lösung partieller Differentialgleichungen


Author: Peter Deuflhard,Martin Weiser
Publisher: Walter de Gruyter
ISBN: 3110218038
Category: Mathematics
Page: 442
View: 3789

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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.

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: 3144

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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

Mathematical Statistics with Resampling and R


Author: Laura M. Chihara,Tim C. Hesterberg
Publisher: John Wiley & Sons
ISBN: 1118518950
Category: Mathematics
Page: 432
View: 5823

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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.

Wahrscheinlichkeitsrechnung und Statistik


Author: Robert Hafner
Publisher: Springer-Verlag
ISBN: 3709169445
Category: Mathematics
Page: 512
View: 6533

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Das Buch ist eine Einführung in die Wahrscheinlichkeitsrechnung und mathematische Statistik auf mittlerem mathematischen Niveau. Die Pädagogik der Darstellung unterscheidet sich in wesentlichen Teilen – Einführung der Modelle für unabhängige und abhängige Experimente, Darstellung des Suffizienzbegriffes, Ausführung des Zusammenhanges zwischen Testtheorie und Theorie der Bereichschätzung, allgemeine Diskussion der Modellentwicklung – erheblich von der anderer vergleichbarer Lehrbücher. Die Darstellung ist, soweit auf diesem Niveau möglich, mathematisch exakt, verzichtet aber bewußt und ebenfalls im Gegensatz zu vergleichbaren Texten auf die Erörterung von Meßbarkeitsfragen. Der Leser wird dadurch erheblich entlastet, ohne daß wesentliche Substanz verlorengeht. Das Buch will allen, die an der Anwendung der Statistik auf solider Grundlage interessiert sind, eine Einführung bieten, und richtet sich an Studierende und Dozenten aller Studienrichtungen, für die mathematische Statistik ein Werkzeug ist.

Statistik-Workshop für Programmierer


Author: Allen B. Downey
Publisher: O'Reilly Germany
ISBN: 3868993436
Category: Computers
Page: 160
View: 3053

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Wenn Sie programmieren können, beherrschen Sie bereits Techniken, um aus Daten Wissen zu extrahieren. Diese kompakte Einführung in die Statistik zeigt Ihnen, wie Sie rechnergestützt, anstatt auf mathematischem Weg Datenanalysen mit Python durchführen können. Praktischer Programmier-Workshop statt grauer Theorie: Das Buch führt Sie anhand eines durchgängigen Fallbeispiels durch eine vollständige Datenanalyse -- von der Datensammlung über die Berechnung statistischer Kennwerte und Identifikation von Mustern bis hin zum Testen statistischer Hypothesen. Gleichzeitig werden Sie mit statistischen Verteilungen, den Regeln der Wahrscheinlichkeitsrechnung, Visualisierungsmöglichkeiten und vielen anderen Arbeitstechniken und Konzepten vertraut gemacht. Statistik-Konzepte zum Ausprobieren: Entwickeln Sie über das Schreiben und Testen von Code ein Verständnis für die Grundlagen von Wahrscheinlichkeitsrechnung und Statistik: Überprüfen Sie das Verhalten statistischer Merkmale durch Zufallsexperimente, zum Beispiel indem Sie Stichproben aus unterschiedlichen Verteilungen ziehen. Nutzen Sie Simulationen, um Konzepte zu verstehen, die auf mathematischem Weg nur schwer zugänglich sind. Lernen Sie etwas über Themen, die in Einführungen üblicherweise nicht vermittelt werden, beispielsweise über die Bayessche Schätzung. Nutzen Sie Python zur Bereinigung und Aufbereitung von Rohdaten aus nahezu beliebigen Quellen. Beantworten Sie mit den Mitteln der Inferenzstatistik Fragestellungen zu realen Daten.