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Books in Statistics

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11-20 of 145 results in All results

Statistical Methods

  • 4th Edition
  • April 16, 2021
  • Donna L. Mohr + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 2 3 0 4 3 - 5
  • eBook
    9 7 8 - 0 - 3 2 3 - 8 9 9 8 8 - 8
Statistical Methods, Fourth Edition, is designed to introduce students to a wide-range of popular and practical statistical techniques. Requiring a minimum of advanced mathematics, it is suitable for undergraduates in statistics, or graduate students in the physical, life, and social sciences. By providing an overview of statistical reasoning, this text equips readers with the insight needed to summarize data, recognize good experimental designs, implement appropriate analyses, and arrive at sound interpretations of statistical results.

Basic Statistics with R

  • 1st Edition
  • February 20, 2021
  • Stephen C. Loftus
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 2 0 7 8 8 - 8
  • eBook
    9 7 8 - 0 - 1 2 - 8 2 0 9 2 6 - 4
Basic Statistics with R: Reaching Decisions with Data provides an understanding of the processes at work in using data for results. Sections cover data collection and discuss exploratory analyses, including visual graphs, numerical summaries, and relationships between variables - basic probability, and statistical inference - including hypothesis testing and confidence intervals. All topics are taught using real-data drawn from various fields, including economics, biology, political science and sports. Using this wide variety of motivating examples allows students to directly connect and make statistics essential to their field of interest, rather than seeing it as a separate and ancillary knowledge area. In addition to introducing students to statistical topics using real data, the book provides a gentle introduction to coding, having the students use the statistical language and software R. Students learn to load data, calculate summary statistics, create graphs and do statistical inference using R with either Windows or Macintosh machines.

Introduction to Probability and Statistics for Engineers and Scientists

  • 6th Edition
  • September 11, 2020
  • Sheldon M. Ross
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 8 2 4 3 4 6 - 6
  • eBook
    9 7 8 - 0 - 1 2 - 8 1 7 7 4 7 - 1
Introduction to Probability and Statistics for Engineers and Scientists, Sixth Edition, uniquely emphasizes how probability informs statistical problems, thus helping readers develop an intuitive understanding of the statistical procedures commonly used by practicing engineers and scientists. Utilizing real data from actual studies across life science, engineering, computing and business, this useful introduction supports reader comprehension through a wide variety of exercises and examples. End-of-chapter reviews of materials highlight key ideas, also discussing the risks associated with the practical application of each material. In the new edition, coverage includes information on Big Data and the use of R. This book is intended for upper level undergraduate and graduate students taking a probability and statistics course in engineering programs as well as those across the biological, physical and computer science departments. It is also appropriate for scientists, engineers and other professionals seeking a reference of foundational content and application to these fields.

Mathematical Statistics with Applications in R

  • 3rd Edition
  • May 14, 2020
  • Kandethody M. Ramachandran + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 1 7 8 1 5 - 7
  • eBook
    9 7 8 - 0 - 1 2 - 8 1 7 8 1 6 - 4
Mathematical Statistics with Applications in R, Third 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 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. Step-by-step procedure to solve real problems make the topics very accessible.

Statistics for Biomedical Engineers and Scientists

  • 1st Edition
  • May 18, 2019
  • Andrew P. King + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 0 8 - 1 0 2 9 3 9 - 8
  • eBook
    9 7 8 - 0 - 0 8 - 1 0 2 9 4 0 - 4
Statistics for Biomedical Engineers and Scientists: How to Analyze and Visualize Data provides an intuitive understanding of the concepts of basic statistics, with a focus on solving biomedical problems. Readers will learn how to understand the fundamental concepts of descriptive and inferential statistics, analyze data and choose an appropriate hypothesis test to answer a given question, compute numerical statistical measures and perform hypothesis tests ‘by hand’, and visualize data and perform statistical analysis using MATLAB. Practical activities and exercises are provided, making this an ideal resource for students in biomedical engineering and the biomedical sciences who are in a course on basic statistics.

Biostatistics for Medical and Biomedical Practitioners

  • 2nd Edition
  • March 19, 2019
  • Julien I. E. Hoffman
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 1 7 0 8 4 - 7
  • eBook
    9 7 8 - 0 - 1 2 - 8 1 7 0 8 5 - 4
Basic Biostatistics for Medical and Biomedical Practitioners, Second Edition makes it easier to plan experiments, with an emphasis on sample size. It also shows what choices are available when simple tests are unsuitable and offers investigators an overview of how the kinds of complex tests that they won't do on their own work. The second edition presents a new, revised and enhanced version of the chapters, taking into consideration new developments and tools available, discussing topics, such as the basic aspects of statistics, continuous distributions, hypothesis testing, discrete distributions, probability in epidemiology and medical diagnosis, comparing means, regression and correlation. This book is a valuable source for students and researchers looking to expand or refresh their understanding of statistics as it applies to the biomedical and research fields. Based on the author’s 40+ years of teaching statistics to medical fellows and biomedical researchers across a wide range of fields, it is a valuable source for researchers who need to understand more about biostatistics to apply it to their work.

Introduction to Probability Models

  • 12th Edition
  • March 9, 2019
  • Sheldon M. Ross
  • English
  • eBook
    9 7 8 - 0 - 1 2 - 8 1 4 3 4 7 - 6
Introduction to Probability Models, Twelfth Edition, is the latest version of Sheldon Ross's classic bestseller. This trusted book introduces the reader to elementary probability modelling and stochastic processes and shows how probability theory can be applied in fields such as engineering, computer science, management science, the physical and social sciences and operations research. The hallmark features of this text have been retained in this edition, including a superior writing style and excellent exercises and examples covering the wide breadth of coverage of probability topics. In addition, many real-world applications in engineering, science, business and economics are included.

Integrated Population Biology and Modeling Part B

  • 1st Edition
  • Volume 40
  • February 5, 2019
  • Arni S.R. Srinivasa Rao + 1 more
  • English
  • Hardback
    9 7 8 - 0 - 4 4 4 - 6 4 1 5 2 - 6
  • eBook
    9 7 8 - 0 - 4 4 4 - 6 4 1 5 3 - 3
Integrated Population Biology and Modeling: Part B, Volume 40, offers very delicately complex and precise realities of quantifying modern and traditional methods of understanding populations and population dynamics, with this updated release focusing on Prey-predator animal models, Back projections, Evolutionary Biology computations, Population biology of collective behavior and bio patchiness, Collective behavior, Population biology through data science, Mathematical modeling of multi-species mutualism: new insights, remaining challenges and applications to ecology, Population Dynamics of Manipur, Stochastic Processes and Population Dynamics Models: The Mechanisms for Extinction, Persistence and Resonance, Theories of Stationary Populations and association with life lived and life left, and more.

Integrated Population Biology and Modeling, Part A

  • 1st Edition
  • Volume 39
  • September 26, 2018
  • Arni S.R. Srinivasa Rao + 1 more
  • English
  • Hardback
    9 7 8 - 0 - 4 4 4 - 6 4 0 7 2 - 7
  • eBook
    9 7 8 - 0 - 4 4 4 - 6 4 0 7 3 - 4
Integrated Population Biology and Modeling: Part A offers very complex and precise realities of quantifying modern and traditional methods of understanding populations and population dynamics. Chapters cover emerging topics of note, including Longevity dynamics, Modeling human-environment interactions, Survival Probabilities from 5-Year Cumulative Life Table Survival Ratios (Tx+5/Tx): Some Innovative Methodological Investigations, Cell migration Models, Evolutionary Dynamics of Cancer Cells, an Integrated approach for modeling of coastal lagoons: A case for Chilka Lake, India, Population and metapopulation dynamics, Mortality analysis: measures and models, Stationary Population Models, Are there biological and social limits to human longevity?, Probability models in biology, Stochastic Models in Population Biology, and more.

Computational Analysis and Understanding of Natural Languages: Principles, Methods and Applications

  • 1st Edition
  • Volume 38
  • August 27, 2018
  • Venkat N. Gudivada + 1 more
  • English
  • Hardback
    9 7 8 - 0 - 4 4 4 - 6 4 0 4 2 - 0
  • eBook
    9 7 8 - 0 - 4 4 4 - 6 4 0 4 3 - 7
Computational Analysis and Understanding of Natural Languages: Principles, Methods and Applications, Volume 38, the latest release in this monograph that provides a cohesive and integrated exposition of these advances and associated applications, includes new chapters on Linguistics: Core Concepts and Principles, Grammars, Open-Source Libraries, Application Frameworks, Workflow Systems, Mathematical Essentials, Probability, Inference and Prediction Methods, Random Processes, Bayesian Methods, Machine Learning, Artificial Neural Networks for Natural Language Processing, Information Retrieval, Language Core Tasks, Language Understanding Applications, and more. The synergistic confluence of linguistics, statistics, big data, and high-performance computing is the underlying force for the recent and dramatic advances in analyzing and understanding natural languages, hence making this series all the more important.