Foundations And Applications Of Statistics
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Foundations And Applications Of Statistics
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Author : Randall Pruim
language : en
Publisher: American Mathematical Soc.
Release Date : 2018-04-04
Foundations And Applications Of Statistics written by Randall Pruim and has been published by American Mathematical Soc. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-04 with Mathematical statistics categories.
Foundations and Applications of Statistics simultaneously emphasizes both the foundational and the computational aspects of modern statistics. Engaging and accessible, this book is useful to undergraduate students with a wide range of backgrounds and career goals. The exposition immediately begins with statistics, presenting concepts and results from probability along the way. Hypothesis testing is introduced very early, and the motivation for several probability distributions comes from p-value computations. Pruim develops the students' practical statistical reasoning through explicit examples and through numerical and graphical summaries of data that allow intuitive inferences before introducing the formal machinery. The topics have been selected to reflect the current practice in statistics, where computation is an indispensible tool. In this vein, the statistical computing environment R is used throughout the text and is integral to the exposition. Attention is paid to developing students' mathematical and computational skills as well as their statistical reasoning. Linear models, such as regression and ANOVA, are treated with explicit reference to the underlying linear algebra, which is motivated geometrically. Foundations and Applications of Statistics discusses both the mathematical theory underlying statistics and practical applications that make it a powerful tool across disciplines. The book contains ample material for a two-semester course in undergraduate probability and statistics. A one-semester course based on the book will cover hypothesis testing and confidence intervals for the most common situations. In the second edition, the R code has been updated throughout to take advantage of new R packages and to illustrate better coding style. New sections have been added covering bootstrap methods, multinomial and multivariate normal distributions, the delta method, numerical methods for Bayesian inference, and nonlinear least squares. Also, the use of matrix algebra has been expanded, but remains optional, providing instructors with more options regarding the amount of linear algebra required.
Econometrics
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Author : P. J. Dhrymes
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Econometrics written by P. J. Dhrymes and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Business & Economics categories.
Foundations Of Statistical Analyses And Applications With Sas
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Author : Michael Falk
language : en
Publisher:
Release Date : 2002-06-01
Foundations Of Statistical Analyses And Applications With Sas written by Michael Falk and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-06-01 with categories.
Basic Concepts In Information Theory And Statistics
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Author : A. M. Mathai
language : en
Publisher:
Release Date : 1975
Basic Concepts In Information Theory And Statistics written by A. M. Mathai and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1975 with Axiomatic set theory categories.
Foundations Of Statistics
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Author : D.G. Rees
language : en
Publisher: CRC Press
Release Date : 1987-09-01
Foundations Of Statistics written by D.G. Rees and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987-09-01 with Mathematics categories.
This text provides a through, straightforward first course on basics statistics. Emphasizing the application of theory, it contains 200 fully worked examples and supplies exercises in each chapter-complete with hints and answers.
Statistical Foundations Of Data Science
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Author : Jianqing Fan
language : en
Publisher: CRC Press
Release Date : 2020-09-21
Statistical Foundations Of Data Science written by Jianqing Fan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-21 with Mathematics categories.
Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.
Foundations Of Statistics For Data Scientists
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Author : ALAN. KATERI AGRESTI (MARIA.)
language : en
Publisher: CRC Press
Release Date : 2024-09-15
Foundations Of Statistics For Data Scientists written by ALAN. KATERI AGRESTI (MARIA.) and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-15 with categories.
Designed as a textbook for a one or two-term introduction to mathematical statistics for students training to become data scientists, Foundations of Statistics for Data Scientists: With R and Python 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 modelling. 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. Alan Agresti, Distinguished Professor Emeritus at the University of Florida, is the author of seven books, including Categorical Data Analysis (Wiley) and Statistics: The Art and Science of Learning from Data (Pearson), and has presented short courses in 35 countries. His awards include an honorary doctorate from De Montfort University (UK) and the Statistician of the Year from the American Statistical Association (Chicago chapter). Maria Kateri, Professor of Statistics and Data Science at the RWTH Aachen University, authored the monograph Contingency Table Analysis: Methods and Implementation Using R (Birkhäuser/Springer) and a textbook on mathematics for economists (in German). She has a long-term experience in teaching statistics courses to students of Data Science, Mathematics, Statistics, Computer Science, and Business Administration and Engineering. "The main goal of this textbook is to present foundational statistical methods and theory that are relevant in the field of data science. The authors depart from the typical approaches taken by many conventional mathematical statistics textbooks by placing more emphasis on providing the students with intuitive and practical interpretations of those methods with the aid of R programming codes...I find its particular strength to be its intuitive presentation of statistical theory and methods without getting bogged down in mathematical details that are perhaps less useful to the practitioners" (Mintaek Lee, Boise State University) "The aspects of this manuscript that I find appealing: 1. The use of real data. 2. The use of R but with the option to use Python. 3. A good mix of theory and practice. 4. The text is well-written with good exercises. 5. The coverage of topics (e.g. Bayesian methods and clustering) that are not usually part of a course in statistics at the level of this book." (Jason M. Graham, University of Scranton)
Statistical Data Analytics
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Author : Walter W. Piegorsch
language : en
Publisher: John Wiley & Sons
Release Date : 2015-08-17
Statistical Data Analytics written by Walter W. Piegorsch and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-08-17 with Mathematics categories.
Statistical Data Analytics Statistical Data Analytics Foundations for Data Mining, Informatics, and Knowledge Discovery A comprehensive introduction to statistical methods for data mining and knowledge discovery Applications of data mining and ‘big data’ increasingly take center stage in our modern, knowledge-driven society, supported by advances in computing power, automated data acquisition, social media development and interactive, linkable internet software. This book presents a coherent, technical introduction to modern statistical learning and analytics, starting from the core foundations of statistics and probability. It includes an overview of probability and statistical distributions, basics of data manipulation and visualization, and the central components of standard statistical inferences. The majority of the text extends beyond these introductory topics, however, to supervised learning in linear regression, generalized linear models, and classification analytics. Finally, unsupervised learning via dimension reduction, cluster analysis, and market basket analysis are introduced. Extensive examples using actual data (with sample R programming code) are provided, illustrating diverse informatic sources in genomics, biomedicine, ecological remote sensing, astronomy, socioeconomics, marketing, advertising and finance, among many others. Statistical Data Analytics: Focuses on methods critically used in data mining and statistical informatics. Coherently describes the methods at an introductory level, with extensions to selected intermediate and advanced techniques. Provides informative, technical details for the highlighted methods. Employs the open-source R language as the computational vehicle – along with its burgeoning collection of online packages – to illustrate many of the analyses contained in the book. Concludes each chapter with a range of interesting and challenging homework exercises using actual data from a variety of informatic application areas. This book will appeal as a classroom or training text to intermediate and advanced undergraduates, and to beginning graduate students, with sufficient background in calculus and matrix algebra. It will also serve as a source-book on the foundations of statistical informatics and data analytics to practitioners who regularly apply statistical learning to their modern data.
The Foundations Of Statistics
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Author : Leonard J. Savage
language : en
Publisher: Courier Corporation
Release Date : 2012-08-29
The Foundations Of Statistics written by Leonard J. Savage and has been published by Courier Corporation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-29 with Mathematics categories.
Classic analysis of the foundations of statistics and development of personal probability, one of the greatest controversies in modern statistical thought. Revised edition. Calculus, probability, statistics, and Boolean algebra are recommended.
The Foundations Of Statistics A Simulation Based Approach
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Author : Shravan Vasishth
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-11-11
The Foundations Of Statistics A Simulation Based Approach written by Shravan Vasishth and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-11-11 with Mathematics categories.
Statistics and hypothesis testing are routinely used in areas (such as linguistics) that are traditionally not mathematically intensive. In such fields, when faced with experimental data, many students and researchers tend to rely on commercial packages to carry out statistical data analysis, often without understanding the logic of the statistical tests they rely on. As a consequence, results are often misinterpreted, and users have difficulty in flexibly applying techniques relevant to their own research — they use whatever they happen to have learned. A simple solution is to teach the fundamental ideas of statistical hypothesis testing without using too much mathematics. This book provides a non-mathematical, simulation-based introduction to basic statistical concepts and encourages readers to try out the simulations themselves using the source code and data provided (the freely available programming language R is used throughout). Since the code presented in the text almost always requires the use of previously introduced programming constructs, diligent students also acquire basic programming abilities in R. The book is intended for advanced undergraduate and graduate students in any discipline, although the focus is on linguistics, psychology, and cognitive science. It is designed for self-instruction, but it can also be used as a textbook for a first course on statistics. Earlier versions of the book have been used in undergraduate and graduate courses in Europe and the US. ”Vasishth and Broe have written an attractive introduction to the foundations of statistics. It is concise, surprisingly comprehensive, self-contained and yet quite accessible. Highly recommended.” Harald Baayen, Professor of Linguistics, University of Alberta, Canada ”By using the text students not only learn to do the specific things outlined in the book, they also gain a skill set that empowers them to explore new areas that lie beyond the book’s coverage.” Colin Phillips, Professor of Linguistics, University of Maryland, USA