This book deals with the autoregressive method for digital processing of random oscillations. The method is based on a one-to-one transformation of the numeric factors of the Yule series model to linear elastic system characteristics. This parametric approach allowed to develop a formal processing procedure from the experimental data to obtain estimates of logarithmic decrement and natural frequency of random oscillations. A straightforward mathematical description of the procedure makes it possible to optimize a discretization of oscillation realizations providing efficient estimates. The derived analytical expressions for confidence intervals of estimates enable a priori evaluation of their accuracy. Experimental validation of the method is also provided. Statistical applications for the analysis of mechanical systems arise from the fact that the loads experienced by machineries and various structures often cannot be described by deterministic vibration theory. Therefore, a sufficient description of real oscillatory processes (vibrations) calls for the use of random functions. In engineering practice, the linear vibration theory (modeling phenomena by common linear differential equations) is generally used. This theory’s fundamental concepts such as natural frequency, oscillation decrement, resonance, etc. are credited for its wide use in different technical tasks. In technical applications two types of research tasks exist: direct and inverse. The former allows to determine stochastic characteristics of the system output X(t) resulting from a random process E(t) when the object model is considered known. The direct task enables to evaluate the effect of an operational environment on the designed object and to predict its operation under various loads. The inverse task is aimed at evaluating the object model on known processes E(t) and X(t), i.e. finding model (equations) factors. This task is usually met at the tests of prototypes to identify (or verify) its model experimentally. To characterize random processes a notion of "shaping dynamic system" is commonly used. This concept allows to consider the observing process as the output of a hypothetical system with the input being stationary Gauss-distributed ("white") noise. Therefore, the process may be exhaustively described in terms of parameters of that system. In the case of random oscillations, the "shaping system" is an elastic system described by the common differential equation of the second order: X ̈(t)+2hX ̇(t)+ ω_0^2 X(t)=E(t), where ω0 = 2π/Т0 is the natural frequency, T0 is the oscillation period, and h is a damping factor. As a result, the process X(t) can be characterized in terms of the system parameters – natural frequency and logarithmic oscillations decrement δ = hT0 as well as the process variance. Evaluation of these parameters is subjected to experimental data processing based on frequency or time-domain representations of oscillations. It must be noted that a concept of these parameters evaluation did not change much during the last century. For instance, in case of the spectral density utilization, evaluation of the decrement values is linked with bandwidth measurements at the points of half-power of the observed oscillations. For a time-domain presentation, evaluation of the decrement requires measuring covariance values delayed by a time interval divisible by T0. Both estimation procedures are derived from a continuous description of research phenomena, so the accuracy of estimates is linked directly to the adequacy of discrete representation of random oscillations. This approach is similar a concept of transforming differential equations to difference ones with derivative approximation by corresponding finite differences. The resulting discrete model, being an approximation, features a methodical error which can be decreased but never eliminated. To render such a presentation more accurate it is imperative to decrease the discretization interval and to increase realization size growing requirements for computing power. The spectral density and covariance function estimates comprise a non-parametric (non-formal) approach. In principle, any non-formal approach is a kind of art i.e. the results depend on the performer’s skills. Due to interference of subjective factors in spectral or covariance estimates of random signals, accuracy of results cannot be properly determined or justified. To avoid the abovementioned difficulties, the application of linear time-series models with well-developed procedures for parameter estimates is more advantageous. A method for the analysis of random oscillations using a parametric model corresponding discretely (no approximation error) with a linear elastic system is developed and presented in this book. As a result, a one-to-one transformation of the model’s numerical factors to logarithmic decrement and natural frequency of random oscillations is established. It allowed to develop a formal processing procedure from experimental data to obtain the estimates of δ and ω0. The proposed approach allows researchers to replace traditional subjective techniques by a formal processing procedure providing efficient estimates with analytically defined statistical uncertainties.

"Getting Started with Tableau 2019.2" is your primer to mastering the latest version of Tableau, a leading tool for data visualization and analysis. Whether you're new to Tableau or looking to upgrade your skills, this book will guide you through both foundational and advanced features, enabling you to create impactful dashboards and visual analytics. What this Book will help me do Understand and utilize the latest features introduced in Tableau 2019.2, including natural language queries in Ask Data. Learn how to connect to diverse data sources, transform data by pivoting fields, and split columns effectively. Gain skills to design intuitive data visualizations and dashboards using various Tableau mark types and properties. Develop interactive and storytelling-based dashboards to communicate insights visually and effectively. Discover methods to securely share your analyses through Tableau Server, enhancing collaboration. Author(s) Tristan Guillevin is an experienced data visualization consultant and an expert in Tableau. Having helped several organizations adopt Tableau for business intelligence, he brings a practical and results-oriented approach to teaching. Tristan's philosophy is to make data accessible and actionable for everyone, no matter their technical background. Who is it for? This book is ideal for Tableau users and data professionals looking to enhance their skills on Tableau 2019.2. If you're passionate about uncovering insights from data but need the right tools to communicate and collaborate effectively, this book is for you. It's suited for those with some prior experience in Tableau but also offers introductory content for newcomers. Whether you're a business analyst, data enthusiast, or BI professional, this guide will build solid foundations and sharpen your Tableau expertise.

Provides a comprehensive and updated study of GARCH models and their applications in finance, covering new developments in the discipline This book provides a comprehensive and systematic approach to understanding GARCH time series models and their applications whilst presenting the most advanced results concerning the theory and practical aspects of GARCH. The probability structure of standard GARCH models is studied in detail as well as statistical inference such as identification, estimation, and tests. The book also provides new coverage of several extensions such as multivariate models, looks at financial applications, and explores the very validation of the models used. GARCH Models: Structure, Statistical Inference and Financial Applications, 2nd Edition features a new chapter on Parameter-Driven Volatility Models, which covers Stochastic Volatility Models and Markov Switching Volatility Models. A second new chapter titled Alternative Models for the Conditional Variance contains a section on Stochastic Recurrence Equations and additional material on EGARCH, Log-GARCH, GAS, MIDAS, and intraday volatility models, among others. The book is also updated with a more complete discussion of multivariate GARCH; a new section on Cholesky GARCH; a larger emphasis on the inference of multivariate GARCH models; a new set of corrected problems available online; and an up-to-date list of references. Features up-to-date coverage of the current research in the probability, statistics, and econometric theory of GARCH models Covers significant developments in the field, especially in multivariate models Contains completely renewed chapters with new topics and results Handles both theoretical and applied aspects Applies to researchers in different fields (time series, econometrics, finance) Includes numerous illustrations and applications to real financial series Presents a large collection of exercises with corrections Supplemented by a supporting website featuring R codes, Fortran programs, data sets and Problems with corrections GARCH Models, 2nd Edition is an authoritative, state-of-the-art reference that is ideal for graduate students, researchers, and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models.

"Principles of Strategic Data Science" is your go-to guide for creating measurable value from data through strategic use of tools and techniques. This book takes you through key theoretical foundations, practical tools, and the managerial perspective necessary to succeed in data science. What this Book will help me do Master the five-phase framework for strategic data science. Learn ways to effectively visualize data information. Explore the role and contributions of a data science manager. Gain clear insights into organizational benefits of data science. Understand the ethical and mathematical boundaries of data analysis. Author(s) Peter Prevos is an accomplished engineer and social scientist with extensive expertise in data science applications. He combines technical insights with social science management practices to design effective data strategies. Known for his clear teaching style, Peter helps professionals integrate theory with practical planning. Who is it for? This book is ideal for data scientists and analysts seeking to deepen their strategic understanding of data science. It's well-suited for intermediate professionals looking to gain insights into data-driven decision making. Readers should have basic programming knowledge in Python or R. Novice managers eager to harness data for organizational goals will also find it valuable.

Applied Supervised Learning with R equips you with the essential knowledge and practical skills to leverage machine learning techniques for solving business problems using R. With this book, you'll gain hands-on experience in implementing various supervised learning models, assessing their performance, and selecting the best-suited method for your objectives. What this Book will help me do Gain expertise in identifying and framing business problems suitable for supervised learning. Acquire skills in data wrangling and visualization using R packages like dplyr and ggplot2. Master techniques for tuning hyperparameters to optimize machine learning models. Understand methods for feature selection and dimensionality reduction to enhance model performance. Learn how to deploy machine learning models to production environments, such as AWS Lambda. Author(s) Karthik Ramasubramanian and Jojo Moolayil are both seasoned data science practitioners and educators who bring a wealth of experience in machine learning and analytics. With a deep understanding of R and its applications in real-world scenarios, they offer practical insights and actionable examples to their readers. Their teaching style focuses on clarity and practical application. Who is it for? This book is ideal for data analysts, data scientists, and data engineers at a beginner to intermediate level who aim to master supervised machine learning with R. Readers should have basic knowledge of statistics, probabilities, and R programming. It is designed for those eager to apply machine learning techniques to real-world problems and improve their decision-making capabilities.

Immerse yourself in 'Hands-On Exploratory Data Analysis with R,' a comprehensive guide designed to hone your skills in data analysis using the powerful R programming language. This book walks you through all essential aspects of exploratory data analysis, from data collection and cleaning to generating insights with statistical and graphical methods, setting you up for success with any dataset. What this Book will help me do Utilize powerful R packages to accelerate your data analysis workflow. Effectively import, clean, and prepare diverse datasets for analysis. Create informative and visually appealing data visualizations using ggplot2. Generate comprehensive and sharable reports with R Markdown and knitr. Handle multi-factor, optimization, and regression data challenges. Author(s) Radhika Datar and Harish Garg are experienced data analysts and educators specializing in using R for practical data analysis. They have developed this book to share their depth of expertise, offering a detailed yet approachable learning experience. Their combined experience in teaching and applying data analysis in real-world scenarios makes this book an invaluable resource for practitioners. Who is it for? This book is perfect for data enthusiasts looking to strengthen their foundational knowledge in exploratory data analysis. Data analysts, engineers, software developers, and product managers seeking to broaden their skillset in data interpretation and visualization will find this guide extremely beneficial. Whether you're a beginner or already possess basic understanding of data analysis, this book will provide actionable insights to improve your workflow.

Dive into the intricacies of time series analysis and forecasting with R in this comprehensive guide. From foundational concepts to practical implementations, this book equips you with the tools and techniques to analyze, understand, and predict time-dependent data. What this Book will help me do Develop insights by visualizing time-series data and identifying patterns. Master statistical time-series concepts including autocorrelation and moving averages. Learn and implement forecasting models like ARIMA and exponential smoothing. Apply machine learning methodologies for advanced time-series predictions. Work with key R packages for cleaning, manipulating, and analyzing time-series data. Author(s) Rami Krispin is an accomplished statistician and R programmer with extensive experience in data analysis and time-series modeling. His hands-on approach in utilizing R packages and libraries brings clarity to complex time-series concepts. With a passion for teaching and simplifying intricate topics, Rami ensures readers both grasp the theories and apply them effectively. Who is it for? This book is ideal for data analysts, statisticians, and R developers interested in mastering time-series analysis for real-world applications. Designed for readers with a basic understanding of statistics and R programming, it offers a practical approach to learning effective forecasting and data visualization techniques. Professionals aiming to expand their skillset in predictive analytics will find it particularly beneficial.

For decades researchers and programmers have used SAS to analyze, summarize, and report clinical trial data. Now Chris Holland and Jack Shostak have updated their popular Implementing CDISC Using SAS, the first comprehensive book on applying clinical research data and metadata to the Clinical Data Interchange Standards Consortium (CDISC) standards. Implementing CDISC Using SAS: An End-to-End Guide, Revised Second Edition, is an all-inclusive guide on how to implement and analyze the Study Data Tabulation Model (SDTM) and the Analysis Data Model (ADaM) data and prepare clinical trial data for regulatory submission. Updated to reflect the 2017 FDA mandate for adherence to CDISC standards, this new edition covers creating and using metadata, developing conversion specifications, implementing and validating SDTM and ADaM data, determining solutions for legacy data conversions, and preparing data for regulatory submission. The book covers products such as Base SAS, SAS Clinical Data Integration, and the SAS Clinical Standards Toolkit, as well as JMP Clinical. Topics included in this edition include an implementation of the Define-XML 2.0 standard, new SDTM domains, validation with Pinnacle 21 software, event narratives in JMP Clinical, STDM and ADAM metadata spreadsheets, and of course new versions of SAS and JMP software. The second edition was revised to add the latest C-Codes from the most recent release as well as update the make_define macro that accompanies this book in order to add the capability to handle C-Codes. The metadata spreadsheets were updated accordingly. Any manager or user of clinical trial data in this day and age is likely to benefit from knowing how to either put data into a CDISC standard or analyzing and finding data once it is in a CDISC format. If you are one such person--a data manager, clinical and/or statistical programmer, biostatistician, or even a clinician--then this book is for you.

Demonstrates how to solve reliability problems using practical applications of Bayesian models This self-contained reference provides fundamental knowledge of Bayesian reliability and utilizes numerous examples to show how Bayesian models can solve real life reliability problems. It teaches engineers and scientists exactly what Bayesian analysis is, what its benefits are, and how they can apply the methods to solve their own problems. To help readers get started quickly, the book presents many Bayesian models that use JAGS and which require fewer than 10 lines of command. It also offers a number of short R scripts consisting of simple functions to help them become familiar with R coding. Practical Applications of Bayesian Reliability starts by introducing basic concepts of reliability engineering, including random variables, discrete and continuous probability distributions, hazard function, and censored data. Basic concepts of Bayesian statistics, models, reasons, and theory are presented in the following chapter. Coverage of Bayesian computation, Metropolis-Hastings algorithm, and Gibbs Sampling comes next. The book then goes on to teach the concepts of design capability and design for reliability; introduce Bayesian models for estimating system reliability; discuss Bayesian Hierarchical Models and their applications; present linear and logistic regression models in Bayesian Perspective; and more. Provides a step-by-step approach for developing advanced reliability models to solve complex problems, and does not require in-depth understanding of statistical methodology Educates managers on the potential of Bayesian reliability models and associated impact Introduces commonly used predictive reliability models and advanced Bayesian models based on real life applications Includes practical guidelines to construct Bayesian reliability models along with computer codes for all of the case studies JAGS and R codes are provided on an accompanying website to enable practitioners to easily copy them and tailor them to their own applications Practical Applications of Bayesian Reliability is a helpful book for industry practitioners such as reliability engineers, mechanical engineers, electrical engineers, product engineers, system engineers, and materials scientists whose work includes predicting design or product performance.

This series of books collects a diverse array of work that provides the reader with theoretical and applied information on data analysis methods, models, and techniques, along with appropriate applications. Volume 1 begins with an introductory chapter by Gilbert Saporta, a leading expert in the field, who summarizes the developments in data analysis over the last 50 years. The book is then divided into three parts: Part 1 presents clustering and regression cases; Part 2 examines grouping and decomposition, GARCH and threshold models, structural equations, and SME modeling; and Part 3 presents symbolic data analysis, time series and multiple choice models, modeling in demography, and data mining.

This series of books collects a diverse array of work that provides the reader with theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications. Volume 2 begins with an introductory chapter by Gilbert Saporta, a leading expert in the field, who summarizes the developments in data analysis over the last 50 years. The book is then divided into four parts: Part 1 examines (in)dependence relationships, innovation in the Nordic countries, dentistry journals, dependence among growth rates of GDP of V4 countries, emissions mitigation, and five-star ratings; Part 2 investigates access to credit for SMEs, gender-based impacts given Southern Europe’s economic crisis, and labor market transition probabilities; Part 3 looks at recruitment at university job-placement offices and the Program for International Student Assessment; and Part 4 examines discriminants, PageRank, and the political spectrum of Germany.

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. Presents a practical guide on how to visualize and analyze statistical data Provides numerous practical examples and exercises to illustrate the power of statistics in biomedical engineering applications Gives an intuitive understanding of statistical tests Covers practical skills by showing how to perform operations ‘by hand’ and by using MATLAB as a computational tool Includes an online resource with downloadable materials for students and teachers

Learn how graph algorithms can help you leverage relationships within your data to develop intelligent solutions and enhance your machine learning models. With this practical guide,developers and data scientists will discover how graph analytics deliver value, whether they’re used for building dynamic network models or forecasting real-world behavior. Mark Needham and Amy Hodler from Neo4j explain how graph algorithms describe complex structures and reveal difficult-to-find patterns—from finding vulnerabilities and bottlenecksto detecting communities and improving machine learning predictions. You’ll walk through hands-on examples that show you how to use graph algorithms in Apache Spark and Neo4j, two of the most common choices for graph analytics. Learn how graph analytics reveal more predictive elements in today’s data Understand how popular graph algorithms work and how they’re applied Use sample code and tips from more than 20 graph algorithm examples Learn which algorithms to use for different types of questions Explore examples with working code and sample datasets for Spark and Neo4j Create an ML workflow for link prediction by combining Neo4j and Spark

Statistics Essentials For Dummies (9781119590309) was previously published as Statistics Essentials For Dummies (9780470618394). While this version features a new Dummies cover and design, the content is the same as the prior release and should not be considered a new or updated product. Statistics Essentials For Dummies not only provides students enrolled in Statistics I with an excellent high-level overview of key concepts, but it also serves as a reference or refresher for students in upper-level statistics courses. Free of review and ramp-up material, Statistics Essentials For Dummies sticks to the point, with content focused on key course topics only. It provides discrete explanations of essential concepts taught in a typical first semester college-level statistics course, from odds and error margins to confidence intervals and conclusions. This guide is also a perfect reference for parents who need to review critical statistics concepts as they help high school students with homework assignments, as well as for adult learners headed back into the classroom who just need a refresher of the core concepts. The Essentials For Dummies Series Dummies is proud to present our new series, The Essentials For Dummies. Now students who are prepping for exams, preparing to study new material, or who just need a refresher can have a concise, easy-to-understand review guide that covers an entire course by concentrating solely on the most important concepts. From algebra and chemistry to grammar and Spanish, our expert authors focus on the skills students most need to succeed in a subject.

Analyzing Social Media Networks with NodeXL: Insights from a Connected World, Second Edition, provides readers with a thorough, practical and updated guide to NodeXL, the open-source social network analysis (SNA) plug-in for use with Excel. The book analyzes social media, provides a NodeXL tutorial, and presents network analysis case studies, all of which are revised to reflect the latest developments. Sections cover history and concepts, mapping and modeling, the detailed operation of NodeXL, and case studies, including e-mail, Twitter, Facebook, Flickr and YouTube. In addition, there are descriptions of each system and types of analysis for identifying people, documents, groups and events. This book is perfect for use as a course text in social network analysis or as a guide for practicing NodeXL users. Walks users through NodeXL while also explaining the theory and development behind each step Demonstrates how visual analytics research can be applied to SNA tools for the mass market Includes updated case studies from researchers who use NodeXL on popular networks like email, Facebook, Twitter, and Instagram Includes downloadable companion materials and online resources at https://www.smrfoundation.org/nodexl/teaching-with-nodexl/teaching-resources/

The typical data science task in industry starts with an “ask” from the business. But few data scientists have been taught what to do with that ask. This book shows them how to assess it in the context of the business’s goals, reframe it to work optimally for both the data scientist and the employer, and then execute on it. Written by two of the experts who’ve achieved breakthrough optimizations at BuzzFeed, it’s packed with real-world examples that take you from start to finish: from ask to actionable insight. Andrew Kelleher and Adam Kelleher walk you through well-formed, concrete principles for approaching common data science problems, giving you an easy-to-use checklist for effective execution. Using their principles and techniques, you’ll gain deeper understanding of your data, learn how to analyze noise and confounding variables so they don’t compromise your analysis, and save weeks of iterative improvement by planning your projects more effectively upfront. Once you’ve mastered their principles, you’ll put them to work in two realistic, beginning-to-end site optimization tasks. These extended examples come complete with reusable code examples and recommended open-source solutions designed for easy adaptation to your everyday challenges. They will be especially valuable for anyone seeking their first data science job – and everyone who’s found that job and wants to succeed in it.

An essential guide to the concepts of probability theory that puts the focus on models and applications Introduction to Probability offers an authoritative text that presents the main ideas and concepts, as well as the theoretical background, models, and applications of probability. The authors—noted experts in the field—include a review of problems where probabilistic models naturally arise, and discuss the methodology to tackle these problems. A wide-range of topics are covered that include the concepts of probability and conditional probability, univariate discrete distributions, univariate continuous distributions, along with a detailed presentation of the most important probability distributions used in practice, with their main properties and applications. Designed as a useful guide, the text contains theory of probability, de finitions, charts, examples with solutions, illustrations, self-assessment exercises, computational exercises, problems and a glossary. This important text: • Includes classroom-tested problems and solutions to probability exercises • Highlights real-world exercises designed to make clear the concepts presented • Uses Mathematica software to illustrate the text’s computer exercises • Features applications representing worldwide situations and processes • Offers two types of self-assessment exercises at the end of each chapter, so that students may review the material in that chapter and monitor their progress. Written for students majoring in statistics, engineering, operations research, computer science, physics, and mathematics, Introduction to Probability: Models and Applications is an accessible text that explores the basic concepts of probability and includes detailed information on models and applications.

A four-color journey through a complete Tableau visualization Tableau is a popular data visualization tool that’s easy for individual desktop use as well as enterprise. Used by financial analysts, marketers, statisticians, business and sales leadership, and many other job roles to present data visually for easy understanding, it’s no surprise that Tableau is an essential tool in our data-driven economy. Visual Analytics with Tableau is a complete journey in Tableau visualization for a non-technical business user. You can start from zero, connect your first data, and get right into creating and publishing awesome visualizations and insightful dashboards. • Learn the different types of charts you can create • Use aggregation, calculated fields, and parameters • Create insightful maps • Share interactive dashboards Geared toward beginners looking to get their feet wet with Tableau, this book makes it easy and approachable to get started right away.

Dive into the world of data visualization with 'Learn D3.js'. This comprehensive guide introduces D3.js-the leading JavaScript library for creating interactive, data-driven visualizations on the web. By following practical examples, you'll understand core concepts of D3.js, learn to implement various types of visualizations, and develop skills to bring dynamic, responsive graphics to your projects. What this Book will help me do Master the fundamentals of D3.js and use it to produce stunning web-based data visualizations. Bind data to the DOM using D3.js and configure interactive transitions and animations. Gain experience generating a multitude of chart types such as bar, pie, scatter charts, and more. Incorporate user interactivity into your visualizations using D3.js effectively. Work with map-based data visualizations using GIS data and various geographical projections. Author(s) Helder da Rocha is an experienced developer and educator with a passion for data visualization. With a solid background in JavaScript and web technologies, he has crafted this book to make the complexities of D3.js accessible and engaging. His approach emphasizes practical, hands-on learning, nurturing both new and seasoned developers alike. Who is it for? Are you a web developer, designer, or data scientist aiming to create interactive data visualizations for the web? If you have foundational knowledge of HTML, CSS, and JavaScript, this book is your perfect guide. Whether you're dipping your toes into web-based charts or seeking to craft advanced interactive graphics, 'Learn D3.js' is tailored to empower your journey.

If you’re in a hurry to learn D3.js, the leading JavaScript library for web-based graphics and visualization, this book is for you. Written for technically savvy readers with a background in programming or data science, the book moves quickly, emphasizing unifying concepts and patterns. Anticipating common difficulties, author Philipp K. Janert teaches you how to apply D3 to your own problems. Assuming only a general programming background, but no previous experience with contemporary web development, this book explains supporting technologies such as SVG, HTML5, CSS, and the DOM as needed, making it a convenient one-stop resource for a technical audience. Understand D3 selections, the library’s fundamental organizing principle Learn how to create data-driven documents with data binding Create animated graphs and interactive user interfaces Draw figures with curves, shapes, and colors Use the built-in facilities for heatmaps, tree graphs, and networks Simplify your work by writing your own reusable components