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Statistical Inference For Engineers And Data Scientists
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Book Synopsis Statistical Inference for Engineers and Data Scientists by : Pierre Moulin
Download or read book Statistical Inference for Engineers and Data Scientists written by Pierre Moulin and published by Cambridge University Press. This book was released on 2019 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: A mathematically accessible textbook introducing all the tools needed to address modern inference problems in engineering and data science.
Book Synopsis Statistical Methods for Engineers and Scientists by : Robert M. Bethea
Download or read book Statistical Methods for Engineers and Scientists written by Robert M. Bethea and published by Routledge. This book was released on 2018-04-20 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work details the fundamentals of applied statistics and experimental design, presenting a unified approach to data handling that emphasizes the analysis of variance, regression analysis and the use of Statistical Analysis System computer programs. This edition: discusses modern nonparametric methods; contains information on statistical process control and reliability; supplies fault and event trees; furnishes numerous additional end-of-chapter problems and worked examples; and more.
Book Synopsis Computer Age Statistical Inference, Student Edition by : Bradley Efron
Download or read book Computer Age Statistical Inference, Student Edition written by Bradley Efron and published by Cambridge University Press. This book was released on 2021-06-17 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.
Book Synopsis Practical Statistics for Data Scientists by : Peter Bruce
Download or read book Practical Statistics for Data Scientists written by Peter Bruce and published by "O'Reilly Media, Inc.". This book was released on 2017-05-10 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data
Download or read book Data Analysis written by Siegmund Brandt and published by Springer Science & Business Media. This book was released on 2014-02-14 with total page 523 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fourth edition of this successful textbook presents a comprehensive introduction to statistical and numerical methods for the evaluation of empirical and experimental data. Equal weight is given to statistical theory and practical problems. The concise mathematical treatment of the subject matter is illustrated by many examples and for the present edition a library of Java programs has been developed. It comprises methods of numerical data analysis and graphical representation as well as many example programs and solutions to programming problems. The book is conceived both as an introduction and as a work of reference. In particular it addresses itself to students, scientists and practitioners in science and engineering as a help in the analysis of their data in laboratory courses, in working for bachelor or master degrees, in thesis work, and in research and professional work.
Book Synopsis Statistical Inference by : George Casella
Download or read book Statistical Inference written by George Casella and published by CRC Press. This book was released on 2024-05-23 with total page 1746 pages. Available in PDF, EPUB and Kindle. Book excerpt: This classic textbook builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and natural extensions, and consequences, of previous concepts. It covers all topics from a standard inference course including: distributions, random variables, data reduction, point estimation, hypothesis testing, and interval estimation. Features The classic graduate-level textbook on statistical inference Develops elements of statistical theory from first principles of probability Written in a lucid style accessible to anyone with some background in calculus Covers all key topics of a standard course in inference Hundreds of examples throughout to aid understanding Each chapter includes an extensive set of graduated exercises Statistical Inference, Second Edition is primarily aimed at graduate students of statistics, but can be used by advanced undergraduate students majoring in statistics who have a solid mathematics background. It also stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures, while less focused on formal optimality considerations. This is a reprint of the second edition originally published by Cengage Learning, Inc. in 2001.
Book Synopsis Probability and Statistics for Engineers and Scientists by : Ronald E. Walpole
Download or read book Probability and Statistics for Engineers and Scientists written by Ronald E. Walpole and published by . This book was released on 2012 with total page 791 pages. Available in PDF, EPUB and Kindle. Book excerpt: This classic text provides a rigorous introduction to basic probability theory and statistical inference, illustrated by relevant applications. It assumes a background in calculus and offers a balance of theory and methodology.
Book Synopsis Foundations of Statistics for Data Scientists by : Alan Agresti
Download or read book Foundations of Statistics for Data Scientists written by Alan Agresti and published by CRC Press. This book was released on 2021-11-22 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modeling. The book assumes knowledge of basic calculus, so the presentation can focus on "why it works" as well as "how to do it." Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python. The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists, such as Bayesian inference, generalized linear models for non-normal responses (e.g., logistic regression and Poisson loglinear models), and regularized model fitting. The nearly 500 exercises are grouped into "Data Analysis and Applications" and "Methods and Concepts." Appendices introduce R and Python and contain solutions for odd-numbered exercises. The book's website has expanded R, Python, and Matlab appendices and all data sets from the examples and exercises.
Book Synopsis Introduction to Mechanical Engineering by : Michael Clifford
Download or read book Introduction to Mechanical Engineering written by Michael Clifford and published by CRC Press. This book was released on 2022-12-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Updated throughout for the second edition, Introduction to Mechanical Engineering: Part 1 continues to be the essential text for all first-year undergraduate students, alongside those studying for foundation degrees and HNDs. Written by an experienced team of lecturers at the internationally renowned University of Nottingham, this book provides a comprehensive grounding in the following core engineering topics: thermodynamics, fluid mechanics, solid mechanics, dynamics, electrical and electronic systems and material science. It includes questions and answers for instructors and for self-guided learning. As well as mechanical engineers, this book is highly relevant to civil, automotive and aerospace engineering students.
Book Synopsis Statistical Methods for Engineers and Scientists by : Robert M. Bethea
Download or read book Statistical Methods for Engineers and Scientists written by Robert M. Bethea and published by . This book was released on 1985 with total page 740 pages. Available in PDF, EPUB and Kindle. Book excerpt: Revised and expanded edition of a text that is intended as a basic introductory course in applied statistical methods for students of engineering and the physical sciences at the undergraduate level. Theoretical developments and mathematical treatment of the principles involved are included as needed for understanding of the validity of the techniques presented. The major changes in this edition are a new chapter on statistical process control and reliability, several added nonparametric techniques, and 30 added problems. Annotation copyright by Book News, Inc., Portland, OR
Book Synopsis Statistical Machine Learning by : Richard Golden
Download or read book Statistical Machine Learning written by Richard Golden and published by CRC Press. This book was released on 2020-06-24 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive, batch, minibatch, MCEM, and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics, computer science, electrical engineering, and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students, professional engineers, and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph.D., M.S.E.E., B.S.E.E.) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models.
Book Synopsis Statcrunch Etext for Probability & Statistics for Engineers & Scientists by : Ronald E. Walpole
Download or read book Statcrunch Etext for Probability & Statistics for Engineers & Scientists written by Ronald E. Walpole and published by Pearson. This book was released on 2011-01 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This classic text provides a rigorous introduction to basic probability theory and statistical inference, with a unique balance between theory and methodology. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. This revision focuses on improved clarity and deeper understanding. The StatCrunch eBook offers this text as a Pearson eText. This exciting new version features an embedded version of StatCrunch, allowing students to analyze data sets while reading the book.
Book Synopsis Introduction to Probability and Statistics for Engineers and Scientists by : Sheldon M. Ross
Download or read book Introduction to Probability and Statistics for Engineers and Scientists written by Sheldon M. Ross and published by . This book was released on 1987 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: Elements of probability; Random variables and expectation; Special; random variables; Sampling; Parameter estimation; Hypothesis testing; Regression; Analysis of variance; Goodness of fit and nonparametric testing; Life testing; Quality control; Simulation.
Book Synopsis Probability and Statistics for Engineers and Scientists: Pearson New International Edition by : Ronald E. Walpole
Download or read book Probability and Statistics for Engineers and Scientists: Pearson New International Edition written by Ronald E. Walpole and published by Pearson Higher Ed. This book was released on 2013-08-29 with total page 858 pages. Available in PDF, EPUB and Kindle. Book excerpt: For junior/senior undergraduates taking probability and statistics as applied to engineering, science, or computer science. This classic text provides a rigorous introduction to basic probability theory and statistical inference, with a unique balance between theory and methodology. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. This revision focuses on improved clarity and deeper understanding. This latest edition is also available in as an enhanced Pearson eText. This exciting new version features an embedded version of StatCrunch, allowing students to analyze data sets while reading the book.
Book Synopsis Statistical Inference via Data Science: A ModernDive into R and the Tidyverse by : Chester Ismay
Download or read book Statistical Inference via Data Science: A ModernDive into R and the Tidyverse written by Chester Ismay and published by CRC Press. This book was released on 2019-12-23 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, while focusing on visualization throughout. Features: ● Assumes minimal prerequisites, notably, no prior calculus nor coding experience ● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data journalism website, FiveThirtyEight.com ● Centers on simulation-based approaches to statistical inference rather than mathematical formulas ● Uses the infer package for "tidy" and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods ● Provides all code and output embedded directly in the text; also available in the online version at moderndive.com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics, at both the undergraduate and graduate levels.
Book Synopsis Probability & Statistics for Engineers & Scientists by : Ronald E. Walpole
Download or read book Probability & Statistics for Engineers & Scientists written by Ronald E. Walpole and published by Prentice Hall. This book was released on 2007 with total page 856 pages. Available in PDF, EPUB and Kindle. Book excerpt: This classic text provides a rigorous introduction to basic probability theory and statistical inference, illustrated by relevant applications. It assumes a background in calculus and offers a balance of theory and methodology
Book Synopsis Probability and Statistics for Engineers and Scientists by : Anthony J. Hayter
Download or read book Probability and Statistics for Engineers and Scientists written by Anthony J. Hayter and published by Thomson Brooks/Cole. This book was released on 2012 with total page 826 pages. Available in PDF, EPUB and Kindle. Book excerpt: PROBABILITY AND STATISTICS FOR ENGINEERS AND SCIENTISTS, 4E, International Edition continues the approach that has made previous editions successful. As a teacher and researcher at a premier engineering school, author Tony Hayter is in touch with engineers daily—and understands their vocabulary. The result of this familiarity with the professional community is a clear and readable writing style that readers understand and appreciate, as well as high-interest, relevant examples and data sets that hold readers' attention. A flexible approach to the use of computer tools includes tips for using various software packages as well as computer output (using MINITAB and other programs) that offers practice in interpreting output. Extensive use of examples and data sets illustrates the importance of statistical data collection and analysis for students in a variety of engineering areas as well as for students in physics, chemistry, computing, biology, management, and mathematics.
