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Books in Statistics and probability

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71-80 of 233 results in All results

Essential Statistics, Regression, and Econometrics

  • 2nd Edition
  • June 8, 2015
  • Gary Smith
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 8 0 3 4 5 9 - 0
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 3 4 9 2 - 7
Essential Statistics, Regression, and Econometrics, Second Edition, is innovative in its focus on preparing students for regression/econometrics, and in its extended emphasis on statistical reasoning, real data, pitfalls in data analysis, and modeling issues. This book is uncommonly approachable and easy to use, with extensive word problems that emphasize intuition and understanding. Too many students mistakenly believe that statistics courses are too abstract, mathematical, and tedious to be useful or interesting. To demonstrate the power, elegance, and even beauty of statistical reasoning, this book provides hundreds of new and updated interesting and relevant examples, and discusses not only the uses but also the abuses of statistics. The examples are drawn from many areas to show that statistical reasoning is not an irrelevant abstraction, but an important part of everyday life.

Semi-Markov Models

  • 1st Edition
  • February 2, 2015
  • Yuriy E Obzherin + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 2 2 1 2 - 2
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 2 4 8 6 - 7
Featuring previously unpublished results, Semi-Markov Models: Control of Restorable Systems with Latent Failures describes valuable methodology which can be used by readers to build mathematical models of a wide class of systems for various applications. In particular, this information can be applied to build models of reliability, queuing systems, and technical control. Beginning with a brief introduction to the area, the book covers semi-Markov models for different control strategies in one-component systems, defining their stationary characteristics of reliability and efficiency, and utilizing the method of asymptotic phase enlargement developed by V.S. Korolyuk and A.F. Turbin. The work then explores semi-Markov models of latent failures control in two-component systems. Building on these results, solutions are provided for the problems of optimal periodicity of control execution. Finally, the book presents a comparative analysis of analytical and imitational modeling of some one- and two-component systems, before discussing practical applications of the results

Computational Statistics with R

  • 1st Edition
  • Volume 32
  • November 25, 2014
  • Marepalli B. Rao + 1 more
  • English
  • Hardback
    9 7 8 - 0 - 4 4 4 - 6 3 4 3 1 - 3
  • eBook
    9 7 8 - 0 - 4 4 4 - 6 3 4 4 1 - 2
R is open source statistical computing software. Since the R core group was formed in 1997, R has been extended by a very large number of packages with extensive documentation along with examples freely available on the internet. It offers a large number of statistical and numerical methods and graphical tools and visualization of extraordinarily high quality. R was recently ranked in 14th place by the Transparent Language Popularity Index and 6th as a scripting language, after PHP, Python, and Perl. The book is designed so that it can be used right away by novices while appealing to experienced users as well. Each article begins with a data example that can be downloaded directly from the R website. Data analysis questions are articulated following the presentation of the data. The necessary R commands are spelled out and executed and the output is presented and discussed. Other examples of data sets with a different flavor and different set of commands but following the theme of the article are presented as well. Each chapter predents a hands-on-experience. R has superb graphical outlays and the book brings out the essentials in this arena. The end user can benefit immensely by applying the graphics to enhance research findings. The core statistical methodologies such as regression, survival analysis, and discrete data are all covered.

Doing Bayesian Data Analysis

  • 2nd Edition
  • November 3, 2014
  • John Kruschke
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 4 0 5 8 8 8 - 0
  • eBook
    9 7 8 - 0 - 1 2 - 4 0 5 9 1 6 - 0
Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact high-level scripts that make it easy to run the programs on your own data sets. The book is divided into three parts and begins with the basics: models, probability, Bayes’ rule, and the R programming language. The discussion then moves to the fundamentals applied to inferring a binomial probability, before concluding with chapters on the generalized linear model. Topics include metric-predicted variable on one or two groups; metric-predicted variable with one metric predictor; metric-predicted variable with multiple metric predictors; metric-predicted variable with one nominal predictor; and metric-predicted variable with multiple nominal predictors. The exercises found in the text have explicit purposes and guidelines for accomplishment. This book is intended for first-year graduate students or advanced undergraduates in statistics, data analysis, psychology, cognitive science, social sciences, clinical sciences, and consumer sciences in business.

An Introduction to Probability and Statistical Inference

  • 2nd Edition
  • September 25, 2014
  • George G. Roussas
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 8 0 0 1 1 4 - 1
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 0 4 3 7 - 1
An Introduction to Probability and Statistical Inference, Second Edition, guides you through probability models and statistical methods and helps you to think critically about various concepts. Written by award-winning author George Roussas, this book introduces readers with no prior knowledge in probability or statistics to a thinking process to help them obtain the best solution to a posed question or situation. It provides a plethora of examples for each topic discussed, giving the reader more experience in applying statistical methods to different situations. This text contains an enhanced number of exercises and graphical illustrations where appropriate to motivate the reader and demonstrate the applicability of probability and statistical inference in a great variety of human activities. Reorganized material is included in the statistical portion of the book to ensure continuity and enhance understanding. Each section includes relevant proofs where appropriate, followed by exercises with useful clues to their solutions. Furthermore, there are brief answers to even-numbered exercises at the back of the book and detailed solutions to all exercises are available to instructors in an Answers Manual. This text will appeal to advanced undergraduate and graduate students, as well as researchers and practitioners in engineering, business, social sciences or agriculture.

Mathematical Statistics with Applications in R

  • 2nd Edition
  • August 18, 2014
  • Kandethody M. Ramachandran + 1 more
  • English
  • eBook
    9 7 8 - 0 - 1 2 - 4 1 7 1 3 2 - 9
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.

Introduction to Probability and Statistics for Engineers and Scientists

  • 5th Edition
  • August 14, 2014
  • Sheldon M. Ross
  • English
  • eBook
    9 7 8 - 0 - 1 2 - 3 9 4 8 4 2 - 7
Introduction to Probability and Statistics for Engineers and Scientists, Fifth Edition is a proven text reference that provides a superior introduction to applied probability and statistics for engineering or science majors. The book lays emphasis in the manner in which probability yields insight into statistical problems, ultimately resulting in an intuitive understanding of the statistical procedures most often used by practicing engineers and scientists. Real data from actual studies across life science, engineering, computing and business are incorporated in a wide variety of exercises and examples throughout the text. These examples and exercises are combined with updated problem sets and applications to connect probability theory to everyday statistical problems and situations. The book also contains end of chapter review material that highlights key ideas as well as the risks associated with practical application of the material. Furthermore, there are new additions to proofs in the estimation section as well as new coverage of Pareto and lognormal distributions, prediction intervals, use of dummy variables in multiple regression models, and testing equality of multiple population distributions. This text is intended for upper level undergraduate and graduate students taking a course in probability and statistics for science or engineering, and for scientists, engineers, and other professionals seeking a reference of foundational content and application to these fields.

Systematic Glossary of the Terminology of Statistical Methods

  • 1st Edition
  • June 28, 2014
  • I. Paenson
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 6 2 1 - 0
Systematic Glossary of the Terminology of Statistical Methods focuses on the elaboration of terms used in statistical methods. The publication first elaborates on the subject and basic methods of statistics, collection of statistical data, and classification and tabulation of statistical data. Discussions focus on the basic methods of statistics, units used in statistics, statistical inquiry, and the subject of statistics. The text then ponders on graphic presentation, averages, and measurements of variation. The manuscript examines essential theoretical distributions, moments of a frequency distribution, and statistical inference. Topics include point and interval estimations, binomial and normal distributions, nature of theoretical distributions, and elements of the theory of probability. The text also evaluates the theory of attributes, correlation, analysis of variance, and time series, including decomposition of time series, multiple correlation, and variance analysis with two or more principles of classification. The publication is a valuable reference for readers interested in the terms used in statistical methods.

Order Statistics & Inference

  • 1st Edition
  • June 28, 2014
  • Narayanaswamy Balakrishnan + 1 more
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 7 4 9 - 1
The literature on order statistics and inferenc eis quite extensive and covers a large number of fields ,but most of it is dispersed throughout numerous publications. This volume is the consolidtion of the most important results and places an emphasis on estimation. Both theoretical and computational procedures are presented to meet the needs of researchers, professionals, and students. The methods of estimation discussed are well-illustrated with numerous practical examples from both the physical and life sciences, including sociology,psychology,a nd electrical and chemical engineering. A complete, comprehensive bibliography is included so the book can be used both aas a text and reference.

Statistical Inferences for Stochasic Processes

  • 1st Edition
  • June 28, 2014
  • Ishwar V. Basawa
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 6 1 4 - 2
Statistical Inference Stochastic Processes provides information pertinent to the theory of stochastic processes. This book discusses stochastic models that are increasingly used in scientific research and describes some of their applications. Organized into three parts encompassing 12 chapters, this book begins with an overview of the basic concepts and procedures of statistical inference. This text then explains the inference problems for Galton–Watson process for discrete time and Markov-branching processes for continuous time. Other chapters consider problems of prediction, filtering, and parameter estimation for some simple discrete-time linear stochastic processes. This book discusses as well the ergodic type chains with finite and countable state-spaces and describes some results on birth and death processes that are of a non-ergodic type. The final chapter deals with inference procedures for stochastic processes through sequential procedures. This book is a valuable resource for graduate students.

Related subjects

Probability theory and stochastic processes

Statistics