Articles by "Data Analysis"

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Learn Statistics Today: 50 Solved Equations To Easily Explain Statistics

Math Wizo ... 60 pages - Publisher: Independently Published; (February, 2019) ... Language: English - ISBN-10: 1793436789 - ISBN-13: 978-1793436788.

In this introductory guide, you will easily learn: An overview on data sampling + The calculation of measures of central tendency (mean, median, mode, and range). + How to calculate the measure of data dispersion (standard deviation) on both a population and on a sample. + Calculating the best fit line using the Least Squared Method. + Constructing Box-Whisker Plots, and how to calculate the 3 quartile values. + All about false positive and false negative results and how to calculate confidence intervals using the data information and z-scores. + How to calculate the margin of error and how to conduct Chi square p-values to measure the significance of test results. + And Much More!

A MATLAB Primer for Technical Programming for Materials Science and Engineering

Leonid Burstein ... 284 pages - Publisher: Woodhead Publishing; (February, 2020) ... Language: English - AmazonSIN: B084L22J54.

A MATLAB Primer for Technical Programming for Materials Science and Engineering draws on examples from the field, providing the latest information on this programming tool that is targeted towards materials science. The book enables non-programmers to master MATLAB in order to solve problems in materials science, assuming only a modest mathematical background. In addition, the book introduces programming and technical concepts in a logical manner to help students use MATLAB for subsequent projects. This title offers materials scientists who are non-programming specialists with a coherent and focused introduction to MATLAB. Provides the necessary background, alongside examples drawn from the field, to allow materials scientists to effectively master MATLAB. + Guides the reader through programming and technical concepts in a logical and coherent manner. + Promotes a thorough working familiarity with MATLAB for materials scientists. + Gives the information needed to write efficient and compact programs to solve problems in materials science, tribology, mechanics of materials and other material-related disciplines.

Applied Regression Modeling 2nd Edition

Iain Pardoe .. 346 pages - Publisher: Wiley; 2nd edition (July, 2012) ... Language: English - ISBN-10: 1118097289 - ISBN-13: 978-1118097281.

Fully revised to reflect the latest methodologies and emerging applications, Applied Regression Modeling, Second Edition continues to highlight the benefits of statistical methods, specifically regression analysis and modeling, for understanding, analyzing, and interpreting multivariate data in business, science, and social science applications. The author utilizes a bounty of real-life examples, case studies, illustrations, and graphics to introduce readers to the world of regression analysis using various software packages, including R, SPSS, Minitab, SAS, JMP, and S-PLUS. In a clear and careful writing style, the book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, and Bayesian modeling.

In addition, the Second Edition features clarification and expansion of challenging topics, such as: Transformations, indicator variables, and interaction + Testing model assumptions + Nonconstant variance + Autocorrelation + Variable selection methods + Model building and graphical interpretation. Throughout the book, datasets and examples have been updated and additional problems are included at the end of each chapter, allowing readers to test their comprehension of the presented material. In addition, a related website features the book's datasets, presentation slides, detailed statistical software instructions, and learning resources including additional problems and instructional videos.

Deep Learning by John D. Kelleher

John D. Kelleher ... 296 pages - Publisher: The MIT Press (September, 2019) ... Language: English - ISBN-10: 0262537559 - ISBN-13: 978-0262537551.

An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution.

An Introduction to Categorical Data Analysis 3rd Edition

Alan Agresti ... 400 pages - Publisher: Wiley; 3rd edition (November, 2018) ... Language: English - ISBN-10: 1119405262 - ISBN-13: 978-1119405269.

A valuable new edition of a standard reference: The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data.

Adding to the value in the new edition is: • Illustrations of the use of R software to perform all the analyses in the book • A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis • New sections in many chapters introducing the Bayesian approach for the methods of that chapter • More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets • An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises

Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.

Artificial Intelligence: Modern Magic or Dangerous Future

Yorick Wilks ... 176 pages - Publisher: Icon Books Ltd.; (June, 2019) ... Language: English - AmazonSIN: B07RL4MCXK.

Artificial intelligence has long been a mainstay of science fiction and increasingly it feels as if AI is entering our everyday lives, with technology like Apple’s Siri now prominent, and self-driving cars almost upon us. But what do we actually mean when we talk about ‘AI’? Are the sentient machines of 2001 or The Matrix a real possibility or will real-world artificial intelligence look and feel very different? What has it done for us so far? And what technologies could it yield in the future?. AI expert Yorick Wilks takes a journey through the history of artificial intelligence up to the present day, examining its origins, controversies and achievements, as well as looking into just how it works. He also considers the future, assessing whether these technologies could menace our way of life, but also how we are all likely to benefit from AI applications in the years to come. Entertaining, enlightening, and keenly argued, this is the essential one-stop guide to the AI debate.

Mathematical Statistics with Applications in R 3rd Edition

Kandethody M. Ramachandran, Chris P. Tsokos ... 704 pages - Publisher: Academic Press; 3rd edition (June, 2020) ... Language: English - ISBN-10: 0128178159 - ISBN-13: 978-0128178157.

Mathematical Statistics with Applications in R, Third Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The book covers many modern statistical computational and simulation concepts that are not covered in other texts, such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods, such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. By combining discussion on the theory of statistics with a wealth of real-world applications, the book helps students to approach statistical problem-solving in a logical manner. Step-by-step procedure to solve real problems make the topics very accessible.

Probability: Theory and Examples 5th Edition

Rick Durrett ... 430 pages - Publisher: Cambridge Univ. Press; 5th edition (May, 2019) ... Language: English - ISBN-10: 1108473687 - ISBN-13: 978-1108473682.

This lively introduction to measure-theoretic probability theory covers laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. Concentrating on results that are the most useful for applications, this comprehensive treatment is a rigorous graduate text and reference. Operating under the philosophy that the best way to learn probability is to see it in action, the book contains extended examples that apply the theory to concrete applications. This fifth edition contains a new chapter on multidimensional Brownian motion and its relationship to partial differential equations (PDEs), an advanced topic that is finding new applications. Setting the foundation for this expansion, Chapter 7 now features a proof of ItÃ´'s formula. Key exercises that previously were simply proofs left to the reader have been directly inserted into the text as lemmas. The new edition re-instates discussion about the central limit theorem for martingales and stationary sequences.

Applied Machine Learning for Smart Data Analysis

Nilanjan Dey, Sanjeev Wagh, Parikshit N. Mahalle, Mohd. Shafi Pathan ... 243 pages - Publisher: CRC Press; (May, 2019) ... Language: English - AmazonSIN: B07S4BVPSL.

The book focuses on how machine learning and the Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Ontologies that are used in heterogeneous IoT environments have been discussed including interpretation, context awareness, analyzing various data sources, machine learning algorithms and intelligent services and applications. Further, it includes unsupervised and semi-supervised machine learning techniques with study of semantic analysis and thorough analysis of reviews. Divided into sections such as machine learning, security, IoT and data mining, the concepts are explained with practical implementation including results.

Key Features: Follows an algorithmic approach for data analysis in machine learning + Introduces machine learning methods in applications + Address the emerging issues in computing such as deep learning, machine learning, Internet of Things and data analytics + Focuses on machine learning techniques namely unsupervised and semi-supervised for unseen and seen data sets + Case studies are covered relating to human health, transportation and Internet applications

Web Scraping with Python: Collecting More Data from the Modern Web 2nd Edition

Ryan Mitchell ... 308 pages - Publisher: O'Reilly Media; 2nd edition (April, 2018) ... Language: English - ISBN-10: 1491985577 - ISBN-13: 978-1491985571.

If programming is magic then web scraping is surely a form of wizardry. By writing a simple automated program, you can query web servers, request data, and parse it to extract the information you need. The expanded edition of this practical book not only introduces you web scraping, but also serves as a comprehensive guide to scraping almost every type of data from the modern web. Part I focuses on web scraping mechanics: using Python to request information from a web server, performing basic handling of the server’s response, and interacting with sites in an automated fashion. Part II explores a variety of more specific tools and applications to fit any web scraping scenario you’re likely to encounter. Parse complicated HTML pages + Develop crawlers with the Scrapy framework + Learn methods to store data you scrape + Read and extract data from documents + Clean and normalize badly formatted data + Read and write natural languages + Crawl through forms and logins + Scrape JavaScript and crawl through APIs + Use and write image-to-text software + Avoid scraping traps and bot blockers + Use scrapers to test your website.

Data Analysis with Stata by Prasad Kothari

Prasad Kothari ... 176 pages - Publisher: Packt Publishing; (October, 2015) ... Language: English - AmazonSIN: B013R02BOC.

STATA is an integrated software package that provides you with everything you need for data analysis, data management, and graphics. STATA also provides you with a platform to efficiently perform simulation, regression analysis (linear and multiple) [and custom programming. This book covers data management, graphs visualization, and programming in STATA. Starting with an introduction to STATA and data analytics you'll move on to STATA programming and data management. Next, the book takes you through data visualization and all the important statistical tests in STATA. Linear and logistic regression in STATA is also covered. As you progress through the book, you will explore a few analyses, including the survey analysis, time series analysis, and survival analysis in STATA. You'll also discover different types of statistical modelling techniques and learn how to implement these techniques in STATA.

Predictive Statistics Analysis and Inference Beyond Models

Bertrand S. Clarke, Jennifer L. Clarke ... 656 pages - Publisher: Cambridge Univ. Press; (April, 2018) ... Language: English - ISBN-10: 1107028280 - ISBN-13: 978-1107028289.

All scientific disciplines prize predictive success. Conventional statistical analyses, however, treat prediction as secondary, instead focusing on modeling and hence estimation, testing, and detailed physical interpretation, tackling these tasks before the predictive adequacy of a model is established. This book outlines a fully predictive approach to statistical problems based on studying predictors; the approach does not require predictors correspond to a model although this important special case is included in the general approach. Throughout, the point is to examine predictive performance before considering conventional inference. These ideas are traced through five traditional subfields of statistics, helping readers to refocus and adopt a directly predictive outlook. The book also considers prediction via contemporary 'black box' techniques and emerging data types and methodologies where conventional modeling is so difficult that good prediction is the main criterion available for evaluating the performance of a statistical method. Well-documented open-source R code in a Github repository allows readers to replicate examples and apply techniques to other investigations.

Computer Age Statistical Inference: Algorithms, Evidence, and Data Science

Bradley Efron, Trevor Hastie ... 495 pages - Publisher: Cambridge Univ. Press; (July, 2016) ... Language: English - ISBN-10: 1107149894 - ISBN-13: 978-1107149892.

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', '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? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. 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. The book ends with speculation on the future direction of statistics and data science.

Handbook of Deep Learning Applications

Valentina Emilia Balas, Sanjiban Sekhar Roy, Dharmendra Sharma, Pijush Samui ... 389 pages - Publisher: Springer; (March, 2019) ... Language: English - ISBN-10: 3030114783 - ISBN-13: 978-303011478.

This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.

Understanding Artificial Intelligence: Fundamentals, Use Cases and Methods for a Corporate AI Journey

Ralf T. Kreutzer, Marie Sirrenberg ... 313 pages - Publisher: Springer; (September, 2019) ... Language: English - ISBN-10: 3030252701 - ISBN-13: 978-3030252700.

Artificial Intelligence (AI) will change the lives of people and businesses more fundamentally than many people can even imagine today. This book illustrates the importance of AI in an era of digitalization. It introduces the foundations of AI and explains its benefits and challenges for companies and entire industries. In this regard, AI is approached not just as yet another technology, but as a fundamental innovation, which will spread into all areas of the economy and life, and will disrupt business processes and business models in the years to come. In turn, the book assesses the potential that AI holds, and clarifies the framework that is necessary for pursuing a responsible approach to AI. In a series of best-practice cases, the book subsequently highlights a broad range of sectors and industries, from production to services; from customer service to marketing and sales; and in industries like retail, health care, energy, transportation and many more. In closing, a dedicated chapter outlines a roadmap for a specific corporate AI journey.

An Introduction to Statistics: An Active Learning Approach 2nd Edition

Kieth A. Carlson, Jennifer R. Winquist ... 656 pages - Publisher: SAGE Publications, Inc; 2nd edition (February, 2017) ... Language: English - ISBN-10: 148337873X - ISBN-13: 978-1483378732.

An Introduction to Statistics: An Active Learning Approach, Second Edition by Kieth A. Carlson and Jennifer R. Winquist takes a unique, active approach to teaching and learning introductory statistics that allows students to discover and correct their misunderstandings as chapters progress rather than at their conclusion. Empirically-developed, self-correcting activities reinforce and expand on fundamental concepts, targeting and holding students’ attention. Based on contemporary memory research, this learner-centered approach leads to better long-term retention through active engagement while generating explanations. Along with carefully placed reading questions, this edition includes learning objectives, realistic research scenarios, practice problems, self-test questions, problem sets, and practice tests to help students become more confident in their ability to perform statistics.

Introduction to Probability and Statistics 2nd Edition

Narayan C. Giri ... 537 pages - Publisher: CRC Press; 2nd edition (January, 2019) ... Language: English - AmazonSIN: B07M7XVF7J.

Beginning with the historical background of probability theory, this thoroughly revised text examines all important aspects of mathematical probability - including random variables, probability distributions, characteristic and generating functions, stochatic convergence, and limit theorems - and provides an introduction to various types of statistical problems, covering the broad range of statistical inference.; Requiring a prerequisite in calculus for complete understanding of the topics discussed, the Second Edition contains new material on: univariate distributions; multivariate distributions; large-sample methods; decision theory; and applications of ANOVA.; A primary text for a year-long undergraduate course in statistics (but easily adapted for a one-semester course in probability only), Introduction to Probability and Statistics is for undergraduate students in a wide range of disciplines-statistics, probability, mathematics, social science, economics, engineering, agriculture, biometry, and education.

All of Statistics: A Concise Course in Statistical Inference

Larry Wasserman ... 442 pages - Publisher: Springer; (September, 2004) ... Language: English - ISBN-10: 0387402721 - ISBN-13: 978-0387402727.

Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.

Introduction to Multivariate Analysis

Chris Chatfield, A. Collins ... 248 pages - Publisher: Chapman and Hall/CRC; (May, 1981) ... Language: English - ISBN-10: 9780412160400 - ISBN-13: 978-0412160400.

This book provides an introduction to the analysis of multivariate data.It describes multivariate probability distributions, the preliminary analysisof a large -scale set of data, princ iple component and factor analysis,traditional normal theory material, as well as multidimensional scaling andcluster analysis.Introduction to Multivariate Analysis provides a reasonable blend oftheory and practice. Enough theory is given to introduce the concepts andto make the topics mathematically interesting. In addition the authors discussthe use (and misuse) of the techniques in pra ctice and present appropriatereal-life examples from a variety of areas includ ing agricultural research,soc iology and crim inology. The book should be suitable both for researchworkers and as a text for students taking a course on multivariate analysis.

Introduction to Multivariate Analysis: Linear and Nonlinear Modeling

Sadanori Konishi ... 338 pages - Publisher: Chapman and Hall/CRC; (June, 2014) ... Language: English - ISBN-10: 1466567287 - ISBN-13: 978-1466567283.

Introduction to Multivariate Analysis: Linear and Nonlinear Modeling shows how multivariate analysis is widely used for extracting useful information and patterns from multivariate data and for understanding the structure of random phenomena. Along with the basic concepts of various procedures in traditional multivariate analysis, the book covers nonlinear techniques for clarifying phenomena behind observed multivariate data. It primarily focuses on regression modeling, classification and discrimination, dimension reduction, and clustering. The text thoroughly explains the concepts and derivations of the AIC, BIC, and related criteria and includes a wide range of practical examples of model selection and evaluation criteria. To estimate and evaluate models with a large number of predictor variables, the author presents regularization methods, including the L1 norm regularization that gives simultaneous model estimation and variable selection. For advanced undergraduate and graduate students in statistical science, this text provides a systematic description of both traditional and newer techniques in multivariate analysis and machine learning. It also introduces linear and nonlinear statistical modeling for researchers and practitioners in industrial and systems engineering, information science, life science, and other areas.

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