Advanced Statistical Methods In Data Science

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Advanced Statistical Methods in Data Science

Ding-Geng Chen,Jiahua Chen,Xuewen Lu,Grace Y. Yi,Hao Yu
Advanced Statistical Methods in Data Science Book PDF

Author : Ding-Geng Chen,Jiahua Chen,Xuewen Lu,Grace Y. Yi,Hao Yu
Publisher : Springer
Page : 222 pages
File Size : 42,6 Mb
Release : 2016-11-30
Category : Mathematics
ISBN : 9789811025945

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Advanced Statistical Methods in Data Science by Ding-Geng Chen,Jiahua Chen,Xuewen Lu,Grace Y. Yi,Hao Yu Pdf

This book gathers invited presentations from the 2nd Symposium of the ICSA- CANADA Chapter held at the University of Calgary from August 4-6, 2015. The aim of this Symposium was to promote advanced statistical methods in big-data sciences and to allow researchers to exchange ideas on statistics and data science and to embraces the challenges and opportunities of statistics and data science in the modern world. It addresses diverse themes in advanced statistical analysis in big-data sciences, including methods for administrative data analysis, survival data analysis, missing data analysis, high-dimensional and genetic data analysis, longitudinal and functional data analysis, the design and analysis of studies with response-dependent and multi-phase designs, time series and robust statistics, statistical inference based on likelihood, empirical likelihood and estimating functions. The editorial group selected 14 high-quality presentations from this successful symposium and invited the presenters to prepare a full chapter for this book in order to disseminate the findings and promote further research collaborations in this area. This timely book offers new methods that impact advanced statistical model development in big-data sciences.

Understanding Advanced Statistical Methods

Peter Westfall,Kevin S. S. Henning
Understanding Advanced Statistical Methods Book PDF

Author : Peter Westfall,Kevin S. S. Henning
Publisher : CRC Press
Page : 572 pages
File Size : 46,8 Mb
Release : 2013-04-09
Category : Mathematics
ISBN : 9781466512108

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Understanding Advanced Statistical Methods by Peter Westfall,Kevin S. S. Henning Pdf

Providing a much-needed bridge between elementary statistics courses and advanced research methods courses, Understanding Advanced Statistical Methods helps students grasp the fundamental assumptions and machinery behind sophisticated statistical topics, such as logistic regression, maximum likelihood, bootstrapping, nonparametrics, and Bayesian methods. The book teaches students how to properly model, think critically, and design their own studies to avoid common errors. It leads them to think differently not only about math and statistics but also about general research and the scientific method. With a focus on statistical models as producers of data, the book enables students to more easily understand the machinery of advanced statistics. It also downplays the "population" interpretation of statistical models and presents Bayesian methods before frequentist ones. Requiring no prior calculus experience, the text employs a "just-in-time" approach that introduces mathematical topics, including calculus, where needed. Formulas throughout the text are used to explain why calculus and probability are essential in statistical modeling. The authors also intuitively explain the theory and logic behind real data analysis, incorporating a range of application examples from the social, economic, biological, medical, physical, and engineering sciences. Enabling your students to answer the why behind statistical methods, this text teaches them how to successfully draw conclusions when the premises are flawed. It empowers them to use advanced statistical methods with confidence and develop their own statistical recipes. Ancillary materials are available on the book’s website.

Advanced Statistical Methods for the Analysis of Large Data-Sets

Agostino Di Ciaccio,Mauro Coli,Jose Miguel Angulo Ibanez
Advanced Statistical Methods for the Analysis of Large Data Sets Book PDF

Author : Agostino Di Ciaccio,Mauro Coli,Jose Miguel Angulo Ibanez
Publisher : Springer Science & Business Media
Page : 464 pages
File Size : 54,9 Mb
Release : 2012-03-05
Category : Mathematics
ISBN : 9783642210372

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Advanced Statistical Methods for the Analysis of Large Data-Sets by Agostino Di Ciaccio,Mauro Coli,Jose Miguel Angulo Ibanez Pdf

The theme of the meeting was “Statistical Methods for the Analysis of Large Data-Sets”. In recent years there has been increasing interest in this subject; in fact a huge quantity of information is often available but standard statistical techniques are usually not well suited to managing this kind of data. The conference serves as an important meeting point for European researchers working on this topic and a number of European statistical societies participated in the organization of the event. The book includes 45 papers from a selection of the 156 papers accepted for presentation and discussed at the conference on “Advanced Statistical Methods for the Analysis of Large Data-sets.”

Advanced R Statistical Programming and Data Models

Matt Wiley,Joshua F. Wiley
Advanced R Statistical Programming and Data Models Book PDF

Author : Matt Wiley,Joshua F. Wiley
Publisher : Apress
Page : 649 pages
File Size : 43,7 Mb
Release : 2019-02-20
Category : Computers
ISBN : 9781484228722

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Advanced R Statistical Programming and Data Models by Matt Wiley,Joshua F. Wiley Pdf

Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study. Written by Matt and Joshua F. Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language. You’ll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics. This is a must-have guide and reference on using and programming with the R language. What You’ll LearnConduct advanced analyses in R including: generalized linear models, generalized additive models, mixed effects models, machine learning, and parallel processing Carry out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysis Handle machine learning using R including parallel processing, dimension reduction, and feature selection and classification Address missing data using multiple imputation in R Work on factor analysis, generalized linear mixed models, and modeling intraindividual variability Who This Book Is For Working professionals, researchers, or students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to use R to perform more advanced analytics. Particularly, researchers and data analysts in the social sciences may benefit from these techniques. Additionally, analysts who need parallel processing to speed up analytics are given proven code to reduce time to result(s).

Practical Statistics for Data Scientists

Peter Bruce,Andrew Bruce
Practical Statistics for Data Scientists Book PDF

Author : Peter Bruce,Andrew Bruce
Publisher : "O'Reilly Media, Inc."
Page : 395 pages
File Size : 47,5 Mb
Release : 2017-05-10
Category : Computers
ISBN : 9781491952917

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Practical Statistics for Data Scientists by Peter Bruce,Andrew Bruce Pdf

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

Statistical Methods for Data Analysis in Particle Physics

Luca Lista
Statistical Methods for Data Analysis in Particle Physics Book PDF

Author : Luca Lista
Publisher : Springer
Page : 257 pages
File Size : 53,7 Mb
Release : 2017-10-13
Category : Science
ISBN : 9783319628400

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Statistical Methods for Data Analysis in Particle Physics by Luca Lista Pdf

This concise set of course-based notes provides the reader with the main concepts and tools needed to perform statistical analyses of experimental data, in particular in the field of high-energy physics (HEP). First, the book provides an introduction to probability theory and basic statistics, mainly intended as a refresher from readers’ advanced undergraduate studies, but also to help them clearly distinguish between the Frequentist and Bayesian approaches and interpretations in subsequent applications. More advanced concepts and applications are gradually introduced, culminating in the chapter on both discoveries and upper limits, as many applications in HEP concern hypothesis testing, where the main goal is often to provide better and better limits so as to eventually be able to distinguish between competing hypotheses, or to rule out some of them altogether. Many worked-out examples will help newcomers to the field and graduate students alike understand the pitfalls involved in applying theoretical concepts to actual data. This new second edition significantly expands on the original material, with more background content (e.g. the Markov Chain Monte Carlo method, best linear unbiased estimator), applications (unfolding and regularization procedures, control regions and simultaneous fits, machine learning concepts) and examples (e.g. look-elsewhere effect calculation).

Advanced and Multivariate Statistical Methods

Craig A. Mertler,Rachel A. Vannatta,Kristina N. LaVenia
Advanced and Multivariate Statistical Methods Book PDF

Author : Craig A. Mertler,Rachel A. Vannatta,Kristina N. LaVenia
Publisher : Taylor & Francis
Page : 351 pages
File Size : 51,5 Mb
Release : 2021-11-29
Category : Psychology
ISBN : 9781000480306

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Advanced and Multivariate Statistical Methods by Craig A. Mertler,Rachel A. Vannatta,Kristina N. LaVenia Pdf

Advanced and Multivariate Statistical Methods, Seventh Edition provides conceptual and practical information regarding multivariate statistical techniques to students who do not necessarily need technical and/or mathematical expertise in these methods. This text has three main purposes. The first purpose is to facilitate conceptual understanding of multivariate statistical methods by limiting the technical nature of the discussion of those concepts and focusing on their practical applications. The second purpose is to provide students with the skills necessary to interpret research articles that have employed multivariate statistical techniques. Finally, the third purpose of AMSM is to prepare graduate students to apply multivariate statistical methods to the analysis of their own quantitative data or that of their institutions. New to the Seventh Edition All references to SPSS have been updated to Version 27.0 of the software. A brief discussion of practical significance has been added to Chapter 1. New data sets have now been incorporated into the book and are used extensively in the SPSS examples. All the SPSS data sets utilized in this edition are available for download via the companion website. Additional resources on this site include several video tutorials/walk-throughs of the SPSS procedures. These "how-to" videos run approximately 5–10 minutes in length. Advanced and Multivariate Statistical Methods was written for use by students taking a multivariate statistics course as part of a graduate degree program, for example in psychology, education, sociology, criminal justice, social work, mass communication, and nursing.

Data Science and Machine Learning

Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman
Data Science and Machine Learning Book PDF

Author : Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman
Publisher : CRC Press
Page : 538 pages
File Size : 55,7 Mb
Release : 2019-11-20
Category : Business & Economics
ISBN : 9781000730777

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Data Science and Machine Learning by Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman Pdf

Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

Design of Experiments and Advanced Statistical Techniques in Clinical Research

Basavarajaiah D. M.,Bhamidipati Narasimha Murthy
Design of Experiments and Advanced Statistical Techniques in Clinical Research Book PDF

Author : Basavarajaiah D. M.,Bhamidipati Narasimha Murthy
Publisher : Springer Nature
Page : 380 pages
File Size : 49,6 Mb
Release : 2020-11-05
Category : Medical
ISBN : 9789811582103

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Design of Experiments and Advanced Statistical Techniques in Clinical Research by Basavarajaiah D. M.,Bhamidipati Narasimha Murthy Pdf

Recent Statistical techniques are one of the basal evidence for clinical research, a pivotal in handling new clinical research and in evaluating and applying prior research. This book explores various choices of statistical tools and mechanisms, analyses of the associations among different clinical attributes. It uses advanced statistical methods to describe real clinical data sets, when the clinical processes being examined are still in the process. This book also discusses distinct methods for building predictive and probability distribution models in clinical situations and ways to assess the stability of these models and other quantitative conclusions drawn by realistic experimental data sets. Design of experiments and recent posthoc tests have been used in comparing treatment effects and precision of the experimentation. This book also facilitates clinicians towards understanding statistics and enabling them to follow and evaluate the real empirical studies (formulation of randomized control trial) that pledge insight evidence base for clinical practices. This book will be a useful resource for clinicians, postgraduates scholars in medicines, clinical research beginners and academicians to nurture high-level statistical tools with extensive scope.

An Introduction to Intermediate and Advanced Statistical Analyses for Sport and Exercise Scientists

Nikos Ntoumanis,Nicholas D. Myers
An Introduction to Intermediate and Advanced Statistical Analyses for Sport and Exercise Scientists Book PDF

Author : Nikos Ntoumanis,Nicholas D. Myers
Publisher : John Wiley & Sons
Page : 312 pages
File Size : 52,9 Mb
Release : 2016-01-19
Category : Medical
ISBN : 9781118962053

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An Introduction to Intermediate and Advanced Statistical Analyses for Sport and Exercise Scientists by Nikos Ntoumanis,Nicholas D. Myers Pdf

"Ntoumanis and Myers have done sport and exercise science researchers and students a tremendous service in producing An Introduction to Intermediate and Advanced Statistical Analyses for Sport and Exercise Scientists. This book has an outstanding compilation of comprehensible chapters dealing with the important concepts and technical minutia of the statistical analyses that sport and exercise science scholars use (or should be using!) in their efforts to conduct meaningful research in the field. It is a resource that all sport and exercise scientists and their students should have on their book shelves." —Robert Eklund, School of Sport, University of Stirling, UK "Motivating, to have a statistics text devoted to enabling researchers studying sport and exercise science to apply the most sophisticated analytical techniques to their data. Authors hit the mark between using technical language as necessary and user-friendly terms or translations to keep users encouraged. Text covers traditional and well-used tools but also less common and more complex tools, but always with familiar examples to make their explanations come alive. As a dynamic systems theorist and developmentalist, I would love to see more researchers in my area create study designs that would enable the use of tools outlined here, such as multilevel structural equation modeling (MSEM) or mediation & moderation analyses, to uncover cascades of relations among subsystems contributing to motor performance, over time. This text can facilitate that outcome." —Beverly D. Ulrich, School of Kinesiology, University of Michigan, USA "The domain of quantitative methods is constantly evolving and expanding. This means that there is tremendous pressure on researchers to stay current, both in terms of best practices and improvements in more traditional methods as well as increasingly complex new methods. With this volume Ntoumanis and Myers present a nice cross-section of both, helping sport and exercise science researchers to address old questions in better ways, and, even more excitingly, to address new questions entirely. I have no doubt that this volume will quickly become a lovingly dog-eared companion for students and researchers, helping them to continue to move the field forward." —Gregory R. Hancock, University of Maryland and Center for Integrated Latent Variable Research (CILVR), USA

Advanced Statistics for the Behavioral Sciences

Jonathon D. Brown
Advanced Statistics for the Behavioral Sciences Book PDF

Author : Jonathon D. Brown
Publisher : Springer
Page : 526 pages
File Size : 46,5 Mb
Release : 2019-05-15
Category : Social Science
ISBN : 331993547X

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Advanced Statistics for the Behavioral Sciences by Jonathon D. Brown Pdf

This book demonstrates the importance of computer-generated statistical analyses in behavioral science research, particularly those using the R software environment. Statistical methods are being increasingly developed and refined by computer scientists, with expertise in writing efficient and elegant computer code. Unfortunately, many researchers lack this programming background, leaving them to accept on faith the black-box output that emerges from the sophisticated statistical models they frequently use. Building on the author’s previous volume, Linear Models in Matrix Form, this text bridges the gap between computer science and research application, providing easy-to-follow computer code for many statistical analyses using the R software environment. The text opens with a foundational section on linear algebra, then covers a variety of advanced topics, including robust regression, model selection based on bias and efficiency, nonlinear models and optimization routines, generalized linear models, and survival and time-series analysis. Each section concludes with a presentation of the computer code used to illuminate the analysis, as well as pointers to packages in R that can be used for similar analyses and nonstandard cases. The accessible code and breadth of topics make this book an ideal tool for graduate students or researchers in the behavioral sciences who are interested in performing advanced statistical analyses without having a sophisticated background in computer science and mathematics.

New Advances in Statistics and Data Science

Ding-Geng Chen,Zhezhen Jin,Gang Li,Yi Li,Aiyi Liu,Yichuan Zhao
New Advances in Statistics and Data Science Book PDF

Author : Ding-Geng Chen,Zhezhen Jin,Gang Li,Yi Li,Aiyi Liu,Yichuan Zhao
Publisher : Springer
Page : 348 pages
File Size : 51,6 Mb
Release : 2018-01-17
Category : Mathematics
ISBN : 9783319694160

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New Advances in Statistics and Data Science by Ding-Geng Chen,Zhezhen Jin,Gang Li,Yi Li,Aiyi Liu,Yichuan Zhao Pdf

This book is comprised of the presentations delivered at the 25th ICSA Applied Statistics Symposium held at the Hyatt Regency Atlanta, on June 12-15, 2016. This symposium attracted more than 700 statisticians and data scientists working in academia, government, and industry from all over the world. The theme of this conference was the “Challenge of Big Data and Applications of Statistics,” in recognition of the advent of big data era, and the symposium offered opportunities for learning, receiving inspirations from old research ideas and for developing new ones, and for promoting further research collaborations in the data sciences. The invited contributions addressed rich topics closely related to big data analysis in the data sciences, reflecting recent advances and major challenges in statistics, business statistics, and biostatistics. Subsequently, the six editors selected 19 high-quality presentations and invited the speakers to prepare full chapters for this book, which showcases new methods in statistics and data sciences, emerging theories, and case applications from statistics, data science and interdisciplinary fields. The topics covered in the book are timely and have great impact on data sciences, identifying important directions for future research, promoting advanced statistical methods in big data science, and facilitating future collaborations across disciplines and between theory and practice.

Statistical Methods for the Social and Behavioural Sciences

David B. Flora
Statistical Methods for the Social and Behavioural Sciences Book PDF

Author : David B. Flora
Publisher : SAGE
Page : 769 pages
File Size : 40,8 Mb
Release : 2017-12-11
Category : Social Science
ISBN : 9781526421920

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Statistical Methods for the Social and Behavioural Sciences by David B. Flora Pdf

Statistical methods in modern research increasingly entail developing, estimating and testing models for data. Rather than rigid methods of data analysis, the need today is for more flexible methods for modelling data. In this logical, easy-to-follow and exceptionally clear book, David Flora provides a comprehensive survey of the major statistical procedures currently used. His innovative model-based approach teaches you how to: Understand and choose the right statistical model to fit your data Match substantive theory and statistical models Apply statistical procedures hands-on, with example data analyses Develop and use graphs to understand data and fit models to data Work with statistical modeling principles using any software package Learn by applying, with input and output files for R, SAS, SPSS, and Mplus. Statistical Methods for the Social and Behavioural Sciences: A Model Based Approach is the essential guide for those looking to extend their understanding of the principles of statistics, and begin using the right statistical modeling method for their own data. It is particularly suited to second or advanced courses in statistical methods across the social and behavioural sciences.

Introduction to Statistical and Machine Learning Methods for Data Science

Carlos Andre Reis Pinheiro,Mike Patetta
Introduction to Statistical and Machine Learning Methods for Data Science Book PDF

Author : Carlos Andre Reis Pinheiro,Mike Patetta
Publisher : SAS Institute
Page : 169 pages
File Size : 42,6 Mb
Release : 2021-08-06
Category : Computers
ISBN : 9781953329622

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Introduction to Statistical and Machine Learning Methods for Data Science by Carlos Andre Reis Pinheiro,Mike Patetta Pdf

Boost your understanding of data science techniques to solve real-world problems Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation and exploration to model assessment and deployment, this book describes every stage of the analytics life cycle, including a comprehensive overview of unsupervised and supervised machine learning techniques. The book guides you through the necessary steps to pick the best techniques and models and then implement those models to successfully address the original business need. No software is shown in the book, and mathematical details are kept to a minimum. This allows you to develop an understanding of the fundamentals of data science, no matter what background or experience level you have.

Basic and Advanced Statistical Tests

Amanda Ross,Victor L. Willson
Basic and Advanced Statistical Tests Book PDF

Author : Amanda Ross,Victor L. Willson
Publisher : Springer
Page : 10 pages
File Size : 40,6 Mb
Release : 2018-01-03
Category : Education
ISBN : 9789463510868

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Basic and Advanced Statistical Tests by Amanda Ross,Victor L. Willson Pdf

This book focuses on extraction of pertinent information from statistical test outputs, in order to write result sections and/or accompanying tables and/or figures. The book is divided into two encompassing sections: Part I – Basic Statistical Tests and Part II – Advanced Statistical Tests. Part I includes 9 basic statistical tests, and Part II includes 7 advanced statistical tests. Each chapter provides the name of a basic or advanced statistical test, a brief description, examples of when to use each, a sample scenario, and a sample results section write-up. Depending on the test and need, most chapters provide a table and/or figure to accompany the write-up. The purpose of the book is to provide researchers with a reference manual for writing results sections and tables/figures in scholarly works. The authors fill a gap in research support manuals by focusing on sample write-ups and tables/figures for given statistical tests. The book assists researchers by eliminating the need to comb through numerous publications to determine necessary information to report, as well as correct APA format to use, at the close of analyses.