Fundamentals of Big Data Network Analysis for Research and Industry

Professionals in all areas – business; government; the physical, life, and social sciences; engineering; medicine, etc. – benefit from using statistical experimental design to better understand their worlds and then use that understanding to improve the products, processes, and programs they are responsible for. This book aims to provide the practitioners of tomorrow with a memorable, easy to read, engaging guide to statistics and experimental design. This book uses examples, drawn from a variety of established texts, and embeds them in a business or scientific context, seasoned with a dash of humor, to emphasize the issues and ideas that led to the experiment and the what-do-we-do-next? steps after the experiment. Graphical data displays are emphasized as means of discovery and communication and formulas are minimized, with a focus on interpreting the results that software produce. The role of subject-matter knowledge, and passion, is also illustrated. The examples do not require specialized knowledge, and the lessons they contain are transferrable to other contexts. Fundamentals of Statistical Experimental Design and Analysis introduces the basic elements of an experimental design, and the basic concepts underlying statistical analyses. Subsequent chapters address the following families of experimental designs: Completely Randomized designs, with single or multiple treatment factors, quantitative or qualitative Randomized Block designs Latin Square designs Split-Unit designs Repeated Measures designs Robust designs Optimal designs Written in an accessible, student-friendly style, this book is suitable for a general audience and particularly for those professionals seeking to improve and apply their understanding of experimental design.

Dive deeper into SPSS Statistics for more efficient, accurate, and sophisticated data analysis and visualization SPSS Statistics for Data Analysis and Visualization goes beyond the basics of SPSS Statistics to show you advanced techniques that exploit the full capabilities of SPSS. The authors explain when and why to use each technique, and then walk you through the execution with a pragmatic, nuts and bolts example. Coverage includes extensive, in-depth discussion of advanced statistical techniques, data visualization, predictive analytics, and SPSS programming, including automation and integration with other languages like R and Python. You'll learn the best methods to power through an analysis, with more efficient, elegant, and accurate code. IBM SPSS Statistics is complex: true mastery requires a deep understanding of statistical theory, the user interface, and programming. Most users don't encounter all of the methods SPSS offers, leaving many little-known modules undiscovered. This book walks you through tools you may have never noticed, and shows you how they can be used to streamline your workflow and enable you to produce more accurate results. Conduct a more efficient and accurate analysis Display complex relationships and create better visualizations Model complex interactions and master predictive analytics Integrate R and Python with SPSS Statistics for more efficient, more powerful code These «hidden tools» can help you produce charts that simply wouldn't be possible any other way, and the support for other programming languages gives you better options for solving complex problems. If you're ready to take advantage of everything this powerful software package has to offer, SPSS Statistics for Data Analysis and Visualization is the expert-led training you need.

The Fundamentals of Scientific Research: An Introductory Laboratory Manual is a laboratory manual geared towards first semester undergraduates enrolled in general biology courses focusing on cell biology. This laboratory curriculum centers on studying a single organism throughout the entire semester – Serratia marcescens, or S. marcescens, a bacterium unique in its production of the red pigment prodigiosin. The manual separates the laboratory course into two separate modules. The first module familiarizes students with the organism and lab equipment by performing growth curves, Lowry protein assays, quantifying prodigiosin and ATP production, and by performing complementation studies to understand the biochemical pathway responsible for prodigiosin production. Students learn to use Microsoft Excel to prepare and present data in graphical format, and how to calculate their data into meaningful numbers that can be compared across experiments. The second module requires that the students employ UV mutagenesis to generate hyper-pigmented mutants of S. marcescens for further characterization. Students use experimental data and protocols learned in the first module to help them develop their own hypotheses, experimental protocols, and to analyze their own data. Before each lab, students are required to answer questions designed to probe their understanding of required pre-laboratory reading materials. Questions also guide the students through the development of hypotheses and predictions. Following each laboratory, students then answer a series of post-laboratory questions to guide them through the presentation and analysis of their data, and how to place their data into the context of primary literature. Students are also asked to review their initial hypotheses and predictions to determine if their conclusions are supportive. A formal laboratory report is also to be completed after each module, in a format similar to that of primary scientific literature. The Fundamentals of Scientific Research: An Introductory Laboratory Manual is an invaluable resource to undergraduates majoring in the life sciences.

Fundamentals of Qualitative Phenomenological Nursing Research Fundamentals of Qualitative Phenomenological Nursing Research is the first book of its kind to specifically link the findings of qualitative research to evidence-based practice, policy, theory, and theory development. Designed for novice researchers, graduate students, and experienced practitioners alike, this comprehensive resource provides up-to-date coverage of research methods and techniques, the use of data analysis software, phenomenological writing and publishing, and more. The text opens with a general introduction to qualitative research and its components, followed by detailed description of the philosophical, paradigmatic, and conceptual aspects of phenomenological inquiry. Subsequent sections address topics including the practical aspects of phenomenological investigations, the concepts of rigor and validity in qualitative studies, and the methods of phenomenological data, collection, reduction, analysis, interpretation, and presentation. Throughout the book, author Brigitte S. Cypress offers expert guidance and real-world tips regarding the challenges researchers encounter when conducting a qualitative study. Provides simple, straightforward descriptions of qualitative research methods with actual phenomenological examples Features numerous in-depth exemplars of the philosophical and paradigmatic aspects of qualitative research from the author’s own studies Includes practical advice on teamwork, mentoring relationships, data organization, and reporting phenomenological studies Presents approaches for dealing with ethical issues, methods for collecting, recording, and storing data, and techniques for analyzing and interpreting findings Examines the role of computer-assisted qualitative data analysis software Fundamentals of Qualitative Phenomenological Nursing Research is a must-have guide for qualitative researchers from any discipline, academics and faculty members, and undergraduate and graduate nursing students wanting to learn more about phenomenology as a research approach.

A practical introduction to qualitative research across fields and disciplines Qualitative Research in Practice offers a hands-on introduction to qualitative research design, methods, data, and analysis. Designed as a companion text for any course involving qualitative research, this book explores the different types of qualitative studies with relevant examples and analysis by the researchers themselves. The workbook format makes it easy to use in the classroom or the field, and the depth of information makes it a valuable resource for students of social work, psychology, counseling, management, education, health care, or any field in which qualitative research is conducted. While quantitative research is primarily concerned with numerical data, qualitative research methods are more flexible, responsive, and open to contextual information. To a qualitative researcher, a situation is defined by the participants’ perspectives, making it the primary method of inquiry for understanding social phenomena through the lens of experience. This book introduces the essentials of qualitative research, bolstered by expert analysis and discussion that provides deeper insight than a traditional textbook format would allow. Understand the fundamental nature of qualitative research Learn how to accurately assess and evaluate qualitative research Explore qualitative research’s many forms and applications Gain insight on qualitative research in a variety of fields and disciplines How does one codify an experience? Is it possible to measure emotion in units? Qualitative research fills the void where numbers cannot reach. It is the best tool we have for studying the unquantifiable aspects of the human experience, and it is an essential tool in a wide variety of fields. Qualitative Research in Practice provides translatable skills in a practical format to quicken your transition from “learning” to “using.”

A practical guide to network meta-analysis with examples and code In the evaluation of healthcare, rigorous methods of quantitative assessment are necessary to establish which interventions are effective and cost-effective. Often a single study will not provide the answers and it is desirable to synthesise evidence from multiple sources, usually randomised controlled trials. This book takes an approach to evidence synthesis that is specifically intended for decision making when there are two or more treatment alternatives being evaluated, and assumes that the purpose of every synthesis is to answer the question “for this pre-identified population of patients, which treatment is ‘best’?” A comprehensive, coherent framework for network meta-analysis (mixed treatment comparisons) is adopted and estimated using Bayesian Markov Chain Monte Carlo methods implemented in the freely available software WinBUGS. Each chapter contains worked examples, exercises, solutions and code that may be adapted by readers to apply to their own analyses. This book can be used as an introduction to evidence synthesis and network meta-analysis, its key properties and policy implications. Examples and advanced methods are also presented for the more experienced reader. Methods used throughout this book can be applied consistently: model critique and checking for evidence consistency are emphasised. Methods are based on technical support documents produced for NICE Decision Support Unit, which support the NICE Methods of Technology Appraisal. Code presented is also the basis for the code used by the ISPOR Task Force on Indirect Comparisons. Includes extensive carefully worked examples, with thorough explanations of how to set out data for use in WinBUGS and how to interpret the output. Network Meta-Analysis for Decision Making will be of interest to decision makers, medical statisticians, health economists, and anyone involved in Health Technology Assessment including the pharmaceutical industry.

Turn jumbles of numbers into graphics, insights, and answers with Excel With Microsoft Excel, you can, well, excel at data analysis. And Excel Data Analysis For Dummies can help, with clear and easy explanations of the mountain of features for creating, visualizing, and analyzing data. PivotTables, charts, what-if analysis, statistical functions—it's all in here, with examples and ideas for Excel users of all skill levels. This latest edition covers the most recent updates to Excel and Microsoft 365. You'll beef up your data skills and learn powerful techniques for turning numbers into knowledge. For students, researchers, and business professionals, Excel is the spreadsheet and data application of choice—and Dummies is the best choice for learning how to make those numbers sing. Learn how to use Excel's built-in data analysis features and write your own functions to explore patterns in your data Create striking charts and visualizations, and discover multiple ways to tell the stories hidden in the numbers Clean up large datasets and identify statistical operations that will answer your questions Perform financial calculations, database operations, and more—without leaving Excel Excel Data Analysis For Dummies is the go-to resource for Excel users who are looking for better ways to crunch the numbers.

The book introduces all the aspects needed for the safe and economic design and analysis of connections using bolted joints in steel structures. This is not treated according to any specific standard but making comparison among the different norms and methodologies used in the engineering practice, e.g. Eurocode, AISC, DIN, BS. Several examples are solved and illustrated in detail, giving the reader all the tools necessary to tackle also complex connection design problems. The book is introductory but also very helpful to advanced and specialist audiences because it covers a large variety of practice demands for connection design. Parts that are not taken to an advanced level are seismic design, welds, interaction with other materials (concrete, wood), and cold formed connections.

Statistics Analysis of Geographical Data: An Introduction provides a comprehensive and accessible introduction to the theory and practice of statistical analysis in geography. It covers a wide range of topics including graphical and numerical description of datasets, probability, calculation of confidence intervals, hypothesis testing, collection and analysis of data using analysis of variance and linear regression. Taking a clear and logical approach, this book examines real problems with real data from the geographical literature in order to illustrate the important role that statistics play in geographical investigations. Presented in a clear and accessible manner the book includes recent, relevant examples, designed to enhance the reader’s understanding.

Praise for the First Edition “…a well-written book on data analysis and data mining that provides an excellent foundation…” —CHOICE “This is a must-read book for learning practical statistics and data analysis…” —Computing Reviews.com A proven go-to guide for data analysis, Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition focuses on basic data analysis approaches that are necessary to make timely and accurate decisions in a diverse range of projects. Based on the authors’ practical experience in implementing data analysis and data mining, the new edition provides clear explanations that guide readers from almost every field of study. In order to facilitate the needed steps when handling a data analysis or data mining project, a step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. The tools to summarize and interpret data in order to master data analysis are integrated throughout, and the Second Edition also features: Updated exercises for both manual and computer-aided implementation with accompanying worked examples New appendices with coverage on the freely available Traceis™ software, including tutorials using data from a variety of disciplines such as the social sciences, engineering, and finance New topical coverage on multiple linear regression and logistic regression to provide a range of widely used and transparent approaches Additional real-world examples of data preparation to establish a practical background for making decisions from data Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition is an excellent reference for researchers and professionals who need to achieve effective decision making from data. The Second Edition is also an ideal textbook for undergraduate and graduate-level courses in data analysis and data mining and is appropriate for cross-disciplinary courses found within computer science and engineering departments.