Practical Data Processing and Analysis Using R

Engaging and accessible, this book offers students a complete guide to using NVivo for qualitative data analysis. Drawing on their wealth of expertise, the authors offer detailed, practical advice tha

Providing information from data preparation and mean, median and mode, to regression, Lisa Daniels and Nicholas Minot use concise descriptions to help students understand the concepts behind statistic

Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use i

This textbook offers an accessible introduction to the theory and numerics of approximation methods, combining classical topics of approximation with recent advances in mathematical signal processing,

Peck, Short, and Olsen's INTRODUCTION TO STATISTICS AND DATA ANALYSIS, 6th Edition stresses interpretation and communication of statistical information through hands-on, activity based learning using

Data integrity is the hottest topic in the pharmaceutical industry. Global regulatory agencies have issued guidance, after guidance after guidance in the past few years, most of which does not offer p

This book presents the R software environment as a key tool for oceanographic computations and provides a rationale for using R over the more widely-used tools of the field such as MATLAB. Kelley prov

This unique guide brings together all the skills you need to get started with data science -- one of the world’s fastest growing fields! Leading data science instructors Michael Freeman and Joel

Panel Data Econometrics with R provides a tutorial for using R in the field of panel data econometrics. Illustrated throughout with examples in econometrics, political science, agriculture and epidemi

The need for HR professionals to understand and apply data analytics is greater than ever. Today’s successful HR professionals must ask insightful questions, understand key terms, and intelligen

This will be a comprehensive, multi-contributed reference work that will detail the latest research and developments in biomedical signal processing related to big data medical analysis. It will descr

Who is most likely to buy and what is the best way to target them? How can businesses improve strategy without identifying the key influencing factors? The second edition of Marketing Analytics enable

The field of medical imaging seen rapid development over the last two decades and has consequently revolutionized the way in which modern medicine is practiced. Diseases and their symptoms are co

This detailed volume provides comprehensive practical guidance on transcriptome data analysis for a variety of scientific purposes. Beginning with general protocols, the collection moves on to explore

Nonlinear Time Series Analysis with R provides a practical guide to emerging empirical techniques allowing practitioners to diagnose whether highly fluctuating and random appearing data are most likel

Get an introduction to functional data structures using R and write more effective code and gain performance for your programs. This book teaches you workarounds because data in functional languages i

This accessible guide presents the astrophysical concepts behind astronomical spectroscopy, covering both the theory and the practical elements of recording, processing, analysing and interpreting your spectra. It covers astronomical objects, such as stars, planets, nebulae, novae, supernovae, and events such as eclipses and comet passages. Suitable for anyone with only a little background knowledge and access to amateur-level equipment, the guide's many illustrations, sketches and figures will help you understand and practise this scientifically important and growing field of amateur astronomy, up to the level of Pro-Am collaborations. Accessible to non-academics, it benefits many groups from novices and learners in astronomy clubs, to advanced students and teachers of astrophysics. This volume is the perfect companion to the Spectral Atlas for Amateur Astronomers, which provides detailed commented spectral profiles of more than 100 astronomical objects.

Understand deep learning, the nuances of its different models, and where these models can be applied.The abundance of data and demand for superior products/services have driven the development of adva

Quantitative Methods in Archaeology Using R is the first hands-on guide to using the R statistical computing system written specifically for archaeologists. It shows how to use the system to analyze many types of archaeological data. Part I includes tutorials on R, with applications to real archaeological data showing how to compute descriptive statistics, create tables, and produce a wide variety of charts and graphs. Part II addresses the major multivariate approaches used by archaeologists, including multiple regression (and the generalized linear model); multiple analysis of variance and discriminant analysis; principal components analysis; correspondence analysis; distances and scaling; and cluster analysis. Part III covers specialized topics in archaeology, including intra-site spatial analysis, seriation, and assemblage diversity.

This unique text helps make sense of big data in engineering applications using tools and techniques from signal processing. It presents fundamental signal processing theories and software implementations, reviews current research trends and challenges, and describes the techniques used for analysis, design and optimization. Readers will learn about key theoretical issues such as data modelling and representation, scalable and low-complexity information processing and optimization, tensor and sublinear algorithms, and deep learning and software architecture, and their application to a wide range of engineering scenarios. Applications discussed in detail include wireless networking, smart grid systems, and sensor networks and cloud computing. This is the ideal text for researchers and practising engineers wanting to solve practical problems involving large amounts of data, and for students looking to grasp the fundamentals of big data analytics.

Quantitative Methods in Archaeology Using R is the first hands-on guide to using the R statistical computing system written specifically for archaeologists. It shows how to use the system to analyze many types of archaeological data. Part I includes tutorials on R, with applications to real archaeological data showing how to compute descriptive statistics, create tables, and produce a wide variety of charts and graphs. Part II addresses the major multivariate approaches used by archaeologists, including multiple regression (and the generalized linear model); multiple analysis of variance and discriminant analysis; principal components analysis; correspondence analysis; distances and scaling; and cluster analysis. Part III covers specialized topics in archaeology, including intra-site spatial analysis, seriation, and assemblage diversity.

Real-world data sets are messy and complicated. Written for students in social science and public management, this authoritative but approachable guide describes all the tools needed to collect data and prepare it for analysis. Offering detailed, step-by-step instructions, it covers collection of many different types of data including web files, APIs, and maps; data cleaning; data formatting; the integration of different sources into a comprehensive data set; and storage using third-party tools to facilitate access and shareability, from Google Docs to GitHub. Assuming no prior knowledge of R and Python, the author introduces programming concepts gradually, using real data sets that provide the reader with practical, functional experience.

Real-world data sets are messy and complicated. Written for students in social science and public management, this authoritative but approachable guide describes all the tools needed to collect data and prepare it for analysis. Offering detailed, step-by-step instructions, it covers collection of many different types of data including web files, APIs, and maps; data cleaning; data formatting; the integration of different sources into a comprehensive data set; and storage using third-party tools to facilitate access and shareability, from Google Docs to GitHub. Assuming no prior knowledge of R and Python, the author introduces programming concepts gradually, using real data sets that provide the reader with practical, functional experience.

Get a jump start on using Azure HDInsight and Hadoop Ecosystem components. As most Hadoop and Big Data projects are written in either Java, Scala, or Python, this book minimizes the effort to learn an

Teach your students to conduct political research using R, the open source programming language and software environment for statistical computing and graphics. An R Companion to Political Analysis by

Electronic health records (EHRs) have become commonplace in the medical profession. Health data are readily captured and permanently stored in a digital fashion, and consequently, are increasingly bei

This book introduces readers to the fundamentals of creating presentation graphics using R, based on 100 detailed and complete scripts. It shows how bar and column charts, population pyramids, Lorenz

Statistics for Psychology Using R comprehensively covers standard statistical methods along with advanced topics such as multivariate techniques, factor analysis, and multiple regression widely used i

You have a mound of data sitting in front of you and a suite of computation tools at your disposal. And yet, you’re stumped as to how to turn that data into insight. Which part of that data actually m

This book inclusively and systematically presents the fundamental methods, models and techniques of practical application of grey data analysis, bringing together the authors’ many years of theoretica

"This book addresses the myriad of practical uses and innovative developments of data envelopment analysis, emphasizing the importance of analyzing productivity by measuring inputs, goals, economic gr

To explore how to conduct research using social media, contributors from a wide range of fields--among them computer engineering, economics, and education--discuss modeling with social data, analytics

Over 140 practical recipes to help you make sense of your data with ease and build production-ready data apps About This Book * Analyze Big Data sets, create attractive visualizations, and manipulate

SAS programming is a creative and iterative process designed to empower you to make the most of your organization's data. This friendly guide provides you with a repertoire of essential SAS tools for data management, whether you are a new or an infrequent user. Most useful to students and programmers with little or no SAS experience, it takes a no-frills, hands-on tutorial approach to getting started with the software. You will find immediate guidance in navigating, exploring, visualizing, cleaning, formatting, and reporting on data using SAS and JMP. Step-by-step demonstrations, screenshots, handy tips, and practical exercises with solutions equip you to explore, interpret, process and summarize data independently, efficiently and effectively.

SAS programming is a creative and iterative process designed to empower you to make the most of your organization's data. This friendly guide provides you with a repertoire of essential SAS tools for data management, whether you are a new or an infrequent user. Most useful to students and programmers with little or no SAS experience, it takes a no-frills, hands-on tutorial approach to getting started with the software. You will find immediate guidance in navigating, exploring, visualizing, cleaning, formatting, and reporting on data using SAS and JMP. Step-by-step demonstrations, screenshots, handy tips, and practical exercises with solutions equip you to explore, interpret, process and summarize data independently, efficiently and effectively.

Big data has presented a number of opportunities across industries. With these opportunities come a number of challenges associated with handling, analyzing, and storing large data sets. One solution

Food products are complex in nature which makes their analysis difficult. Different scientific disciplines such as biochemistry, microbiology, and nutrition, together with engineering concepts are inv

This textbook introduces essential topics and techniques in production and efficiency analysis and shows how to apply these methods using the statistical software R. Numerous small simulations lead to

As statistical methods play key roles in the design and analysis of toxicological studies, toxicologists, especially those not educated in statistics, require good statistical guidance to follow. Writ