## An Introduction to Mathematical Statistics and Its Applications

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

**Publisher:**Pearson

**ISBN:**0134114248

**Category:**Mathematics

**Page:**768

**View:**9709

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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.

## Introduction to Mathematical Statistics

**Author**: Richard J. Larsen,3rd Edition Larsen and Marx,Morris L. Marx,Cram101 Textbook Reviews

**Publisher:**Academic Internet Pub Incorporated

**ISBN:**9781428814431

**Category:**Mathematics

**Page:**92

**View:**2266

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Never HIGHLIGHT a Book Again! Virtually all of the testable terms, concepts, persons, places, and events from the textbook are included. Cram101 Just the FACTS101 studyguides give all of the outlines, highlights, notes, and quizzes for your textbook with optional online comprehensive practice tests. Only Cram101 is Textbook Specific. Accompanys: 9780139223037 .

## Grundzüge der Mikroökonomik

**Author**: Hal R. Varian

**Publisher:**Walter de Gruyter GmbH & Co KG

**ISBN:**3110478315

**Category:**Business & Economics

**Page:**912

**View:**1722

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This textbook succeeds like no other in providing a clear explanation of microeconomic theory while also elucidating how to interpret analytical results. The new edition has been expanded with a chapter on econometrics. In addition, it includes new examples of applied microeconomics based on Silicon Valley firms.

## An introduction to probability theory and its applications

**Author**: William Feller

**Publisher:**John Wiley & Sons

**ISBN:**9780471257080

**Category:**Mathematics

**Page:**528

**View:**5271

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Major changes in this edition include the substitution of probabilistic arguments for combinatorial artifices, and the addition of new sections on branching processes, Markov chains, and the De Moivre-Laplace theorem.

## An Introduction to Probability Theory and Its Applications

**Author**: William Feller

**Publisher:**John Wiley & Sons

**ISBN:**N.A

**Category:**Mathematics

**Page:**704

**View:**5011

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The exponential and the uniform densities; Special densities. Randomization; Densities in higher dimensions. Normal densities and processes; Probability measures and spaces; Probability distributions in Rr; A survey of some important distributions and processes; Laws of large numbers. Aplications in analysis; The basic limit theorems; Infinitely divisible distributions and semi-groups; Markov processes and semi-groups; Renewal theory; Random walks in R1; Laplace transforms. Tauberian theorems. Resolvents; Aplications of Laplace transforms; Characteristic functions; Expansions related to the central limit theorem; Infinitely divisible distributions; Applications of Fourier methods to ramdom walks; harmonic analysis; Answers to problems.

## An Introduction to Statistical Inference and Its Applications with R

**Author**: Michael W. Trosset

**Publisher:**CRC Press

**ISBN:**9781584889489

**Category:**Mathematics

**Page:**496

**View:**8853

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Emphasizing concepts rather than recipes, An Introduction to Statistical Inference and Its Applications with R provides a clear exposition of the methods of statistical inference for students who are comfortable with mathematical notation. Numerous examples, case studies, and exercises are included. R is used to simplify computation, create figures, and draw pseudorandom samples—not to perform entire analyses. After discussing the importance of chance in experimentation, the text develops basic tools of probability. The plug-in principle then provides a transition from populations to samples, motivating a variety of summary statistics and diagnostic techniques. The heart of the text is a careful exposition of point estimation, hypothesis testing, and confidence intervals. The author then explains procedures for 1- and 2-sample location problems, analysis of variance, goodness-of-fit, and correlation and regression. He concludes by discussing the role of simulation in modern statistical inference. Focusing on the assumptions that underlie popular statistical methods, this textbook explains how and why these methods are used to analyze experimental data.

## An introduction to probability and its applications

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

**Publisher:**Prentice Hall

**ISBN:**N.A

**Category:**Business & Economics

**Page:**404

**View:**401

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Drawing heavily on real-world examples and case studies, this volume offers a calculus-based, non-measure theoretic, problem-solving-oriented introduction to probability.

## Studyguide for an Introduction to Mathematical Statistics and Its Applications by Larsen, Richard J., ISBN 9780321831460

**Author**: Cram101 Textbook Reviews

**Publisher:**Cram101

**ISBN:**9781497090231

**Category:**Education

**Page:**138

**View:**8763

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Never HIGHLIGHT a Book Again! Includes all testable terms, concepts, persons, places, and events. Cram101 Just the FACTS101 studyguides gives all of the outlines, highlights, and quizzes for your textbook with optional online comprehensive practice tests. Only Cram101 is Textbook Specific. Accompanies: 9780321831460. This item is printed on demand.

## Einführung in die Statistik der Finanzmärkte

**Author**: Jürgen Franke,Wolfgang Karl Härdle,Christian Matthias Hafner

**Publisher:**Springer-Verlag

**ISBN:**3642170498

**Category:**Business & Economics

**Page:**428

**View:**6803

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## Mathematical Statistics with Applications in R

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

**Publisher:**Elsevier

**ISBN:**012417132X

**Category:**Mathematics

**Page:**826

**View:**926

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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

## Grundbegriffe der Wahrscheinlichkeitsrechnung

**Author**: A. Kolomogoroff

**Publisher:**Springer-Verlag

**ISBN:**3642498884

**Category:**Mathematics

**Page:**62

**View:**3412

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Dieser Buchtitel ist Teil des Digitalisierungsprojekts Springer Book Archives mit Publikationen, die seit den Anfängen des Verlags von 1842 erschienen sind. Der Verlag stellt mit diesem Archiv Quellen für die historische wie auch die disziplingeschichtliche Forschung zur Verfügung, die jeweils im historischen Kontext betrachtet werden müssen. Dieser Titel erschien in der Zeit vor 1945 und wird daher in seiner zeittypischen politisch-ideologischen Ausrichtung vom Verlag nicht beworben.

## Introduction to Probability with Statistical Applications

**Author**: Géza Schay

**Publisher:**Birkhäuser

**ISBN:**3319306200

**Category:**Mathematics

**Page:**385

**View:**6610

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Now in its second edition, this textbook serves as an introduction to probability and statistics for non-mathematics majors who do not need the exhaustive detail and mathematical depth provided in more comprehensive treatments of the subject. The presentation covers the mathematical laws of random phenomena, including discrete and continuous random variables, expectation and variance, and common probability distributions such as the binomial, Poisson, and normal distributions. More classical examples such as Montmort's problem, the ballot problem, and Bertrand’s paradox are now included, along with applications such as the Maxwell-Boltzmann and Bose-Einstein distributions in physics. Key features in new edition: * 35 new exercises * Expanded section on the algebra of sets * Expanded chapters on probabilities to include more classical examples * New section on regression * Online instructors' manual containing solutions to all exercises“/p> Advanced undergraduate and graduate students in computer science, engineering, and other natural and social sciences with only a basic background in calculus will benefit from this introductory text balancing theory with applications. Review of the first edition: This textbook is a classical and well-written introduction to probability theory and statistics. ... the book is written ‘for an audience such as computer science students, whose mathematical background is not very strong and who do not need the detail and mathematical depth of similar books written for mathematics or statistics majors.’ ... Each new concept is clearly explained and is followed by many detailed examples. ... numerous examples of calculations are given and proofs are well-detailed." (Sophie Lemaire, Mathematical Reviews, Issue 2008 m)

## An Introduction to Kolmogorov Complexity and Its Applications

**Author**: Ming Li,Paul Vitanyi

**Publisher:**Springer Science & Business Media

**ISBN:**1475738609

**Category:**Computers

**Page:**550

**View:**2002

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## Statistische Methoden und ihre Anwendungen

**Author**: Erwin Kreyszig

**Publisher:**Ruprecht Gmbh & Company

**ISBN:**9783525407172

**Category:**History

**Page:**451

**View:**7710

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## Mathematical Statistics

**Author**: Wiebe R. Pestman

**Publisher:**Walter de Gruyter

**ISBN:**3110208539

**Category:**Mathematics

**Page:**606

**View:**6324

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This textbook provides a broad and solid introduction to mathematical statistics, including the classical subjects hypothesis testing, normal regression analysis, and normal analysis of variance. In addition, non-parametric statistics and vectorial statistics are considered, as well as applications of stochastic analysis in modern statistics, e.g., Kolmogorov-Smirnov testing, smoothing techniques, robustness and density estimation. For students with some elementary mathematical background. With many exercises. Prerequisites from measure theory and linear algebra are presented.

## An Introduction to Statistics with Python

*With Applications in the Life Sciences*

**Author**: Thomas Haslwanter

**Publisher:**Springer

**ISBN:**3319283162

**Category:**Computers

**Page:**278

**View:**2235

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This textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics. Working code and data for Python solutions for each test, together with easy-to-follow Python examples, can be reproduced by the reader and reinforce their immediate understanding of the topic. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis and an interesting alternative to R. The book is intended for master and PhD students, mainly from the life and medical sciences, with a basic knowledge of statistics. As it also provides some statistics background, the book can be used by anyone who wants to perform a statistical data analysis.

## Statistik-Workshop für Programmierer

**Author**: Allen B. Downey

**Publisher:**O'Reilly Germany

**ISBN:**3868993436

**Category:**Computers

**Page:**160

**View:**5431

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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.

## Linear Statistical Models

**Author**: James H. Stapleton

**Publisher:**John Wiley & Sons

**ISBN:**0470317760

**Category:**Mathematics

**Page:**472

**View:**8936

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Linear Statistical Models Developed and refined over a period of twenty years, the material in this book offers an especially lucid presentation of linear statistical models. These models lead to what is usually called "multiple regression" or "analysis of variance" methodology, which, in turn, opens up a wide range of applications to the physical, biological, and social sciences, as well as to business, agriculture, and engineering. Unlike similar books on this topic, Linear Statistical Models emphasizes the geometry of vector spaces because of the intuitive insights this approach brings to an understanding of the theory. While the focus is on theory, examples of applications, using the SAS and S-Plus packages, are included. Prerequisites include some familiarity with linear algebra, and probability and statistics at the postcalculus level. Major topics covered include: * Methods of study of random vectors, including the multivariate normal, chi-square, t and F distributions, central and noncentral * The linear model and the basic theory of regression analysis and the analysis of variance * Multiple regression methods, including transformations, analysis of residuals, and asymptotic theory for regression analysis. Separate sections are devoted to robust methods and to the bootstrap. * Simultaneous confidence intervals: Bonferroni, Scheffe, Tukey, and Bechhofer * Analysis of variance, with two- and three-way analysis of variance * Random component models, nested designs, and balanced incomplete block designs * Analysis of frequency data through log-linear models, with emphasis on vector space viewpoint. This chapter alone is sufficient for a course on the analysis of frequency data.