Primer For Data Analytics And Graduate Study In Statistics
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Primer for Data Analytics and Graduate Study in Statistics by Douglas Wolfe,Grant Schneider Pdf
This book is specially designed to refresh and elevate the level of understanding of the foundational background in probability and distributional theory required to be successful in a graduate-level statistics program. Advanced undergraduate students and introductory graduate students from a variety of quantitative backgrounds will benefit from the transitional bridge that this volume offers, from a more generalized study of undergraduate mathematics and statistics to the career-focused, applied education at the graduate level. In particular, it focuses on growing fields that will be of potential interest to future M.S. and Ph.D. students, as well as advanced undergraduates heading directly into the workplace: data analytics, statistics and biostatistics, and related areas.
This book is intended for psychology majors and graduate students who are conducting experiments for the first time and are faced with the task of making sense out of their data. This much needed "how-to-do-it" text illustrates the application of statistical methods to the data from small samples. It also serves as a handbook, with twenty-two tables presented at the end of the text that will allow the student to carry out virtually every computation necessary in analyzing his data. Almost all of the examples and illustrations are drawn from actual experiments so that the student can see how professional scientists examine their data. The book also shows students the kinds of data that are encountered in psychological research, the kinds of questions investigators seek to answer, and how these questions are approached. The author asserts that statistics is not an abstract discipline but a tool in research. However, the book also imparts a philosophy of data analysis and its meaning, a concern for questions of the function of data analysis and the interpretations that legitimately can be drawn from data. In brief, Data Analysis asks: What kinds of data are met in psychological research? What can we do with these data? What can we conclude as a result of this doing? The book will be invaluable for students who, even though they may have taken a previous statistics course, are still unsure of what statistical techniques should be used in interpreting their data.
A Primer in Longitudinal Data Analysis by Toon W Taris Pdf
`The author has done a remarkable job of writing a very accessible introduction to a broad literature. As such, he should be congratulated on achieving his objective to provide the "ideal primer for this growing area of social research"′ - Kwantitatieve Methoden This accessible introduction to the theory and practice of longitudinal research takes the reader through the strengths and weaknesses of this kind of research, making clear: how to design a longitudinal study; how to collect data most effectively; how to make the best use of statistical techniques; and how to interpret results. Although the book provides a broad overview of the field, the focus is always on the practical issues arising out of longitudinal research. This book supplies the student with all that they need to get started and acts as a manual for dealing with opportunities and pitfalls. It is the ideal primer for this growing area of social research.
Statistics for Library and Information Services by Alon Friedman Pdf
Statistics for Library and Information Services, written for non-statisticians, provides logical, user-friendly, and step-by-step instructions to make statistics more accessible for students and professionals in the field of Information Science. It emphasizes concepts of statistical theory and data collection methodologies, but also extends to the topics of visualization creation and display, so that the reader will be able to better conduct statistical analysis and communicate his/her findings. The book is tailored for information science students and professionals. It has specific examples of dataset sets, scripts, design modules, data repositories, homework assignments, and a glossary lexicon that matches the field of Information Science. The textbook provides a visual road map that is customized specifically for Information Science instructors, students, and professionals regarding statistics and visualization. Each chapter in the book includes full-color illustrations on how to use R for the statistical model that particular chapter will cover. This book is arranged in 17 chapters, which are organized into five main sections: the first section introduces research design and data collection; the second section discusses basic statistical concepts, including descriptive, bivariate, time series, and regression analyses; section 3 covers the subject of visualization creation using Open Source R; section 4 covers decision making from the analysis; and the last section provides examples and references. Every chapter illustrates how to use Open Source R and features two subsections for the major ideas of the chapter: its statistical model and its visual representation. The statistical model captures the main statistical formulas/theories covered in each chapter, while the visual representation addresses the subject of the types of visualization that are produced from the statistical analysis model covered in that particular chapter. Don’t miss the book’s companion Web site at www.statisticsforlis.org
A Primer in Econometric Theory by John Stachurski Pdf
A concise treatment of modern econometrics and statistics, including underlying ideas from linear algebra, probability theory, and computer programming. This book offers a cogent and concise treatment of econometric theory and methods along with the underlying ideas from statistics, probability theory, and linear algebra. It emphasizes foundations and general principles, but also features many solved exercises, worked examples, and code listings. After mastering the material presented, readers will be ready to take on more advanced work in different areas of quantitative economics and to understand papers from the econometrics literature. The book can be used in graduate-level courses on foundational aspects of econometrics or on fundamental statistical principles. It will also be a valuable reference for independent study. One distinctive aspect of the text is its integration of traditional topics from statistics and econometrics with modern ideas from data science and machine learning; readers will encounter ideas that are driving the current development of statistics and increasingly filtering into econometric methodology. The text treats programming not only as a way to work with data but also as a technique for building intuition via simulation. Many proofs are followed by a simulation that shows the theory in action. As a primer, the book offers readers an entry point into the field, allowing them to see econometrics as a whole rather than as a profusion of apparently unrelated ideas.
Basic Statistics by Olive Jean Dunn,Virginia A. Clark Pdf
New Edition of a Classic Guide to Statistical Applications in the Biomedical Sciences In the last decade, there have been significant changes in the way statistics is incorporated into biostatistical, medical, and public health research. Addressing the need for a modernized treatment of these statistical applications, Basic Statistics, Fourth Edition presents relevant, up-to-date coverage of research methodology using careful explanations of basic statistics and how they are used to address practical problems that arise in the medical and public health settings. Through concise and easy-to-follow presentations, readers will learn to interpret and examine data by applying common statistical tools, such as sampling, random assignment, and survival analysis. Continuing the tradition of its predecessor, this new edition outlines a thorough discussion of different kinds of studies and guides readers through the important, related decision-making processes such as determining what information is needed and planning the collections process. The book equips readers with the knowledge to carry out these practices by explaining the various types of studies that are commonly conducted in the fields of medical and public health, and how the level of evidence varies depending on the area of research. Data screening and data entry into statistical programs is explained and accompanied by illustrations of statistical analyses and graphs. Additional features of the Fourth Edition include: A new chapter on data collection that outlines the initial steps in planning biomedical and public health studies A new chapter on nonparametric statistics that includes a discussion and application of the Sign test, the Wilcoxon Signed Rank test, and the Wilcoxon Rank Sum test and its relationship to the Mann-Whitney U test An updated introduction to survival analysis that includes the Kaplan Meier method for graphing the survival function and a brief introduction to tests for comparing survival functions Incorporation of modern statistical software, such as SAS, Stata, SPSS, and Minitab into the presented discussion of data analysis Updated references at the end of each chapter Basic Statistics, Fourth Edition is an ideal book for courses on biostatistics, medicine, and public health at the upper-undergraduate and graduate levels. It is also appropriate as a reference for researchers and practitioners who would like to refresh their fundamental understanding of statistical techniques.
This book is intended for psychology majors and graduate students who are conducting experiments for the first time and are faced with the task of making sense out of their data. This much needed "how-to-do-it" text illustrates the application of statistical methods to the data from small samples. It also serves as a handbook, with twenty-two tables presented at the end of the text that will allow the student to carry out virtually every computation necessary in analyzing his data. Almost all of the examples and illustrations are drawn from actual experiments so that the student can see how professional scientists examine their data. The book also shows students the kinds of data that are encountered in psychological research, the kinds of questions investigators seek to answer, and how these questions are approached. The author asserts that statistics is not an abstract discipline but a tool in research. However, the book also imparts a philosophy of data analysis and its meaning, a concern for questions of the function of data analysis and the interpretations that legitimately can be drawn from data. In brief, Data Analysis asks: What kinds of data are met in psychological research? What can we do with these data? What can we conclude as a result of this doing? The book will be invaluable for students who, even though they may have taken a previous statistics course, are still unsure of what statistical techniques should be used in interpreting their data. Edward L. Wike was educated at the University of California at Los Angeles, where he received his Ph.D. in experimental psychology in 1952. He has contributed many articles to psychology journals and is currently Professor of Psychology, University of Kansas. He was a member of American Psychology Association, Sigma Xi, as well as the Psychonomic Society. He was named Outstanding Educator in America in 1975.
Statistics in MATLAB by MoonJung Cho,Wendy L. Martinez Pdf
This primer provides an accessible introduction to MATLAB version 8 and its extensive functionality for statistics. Fulfilling the need for a practical user's guide, the book covers capabilities in the main MATLAB package, the Statistics Toolbox, and the student version of MATLAB, presenting examples of how MATLAB can be used to analyze data. It explains how to determine what method should be used for analysis, and includes figures, visual aids, and access to a companion website with data sets and additional examples.
Learn the concepts and methods for creating economic and business value with digital analytics, mobile analytics, web analytics, and market research and social media data. In Digital Analytics Primer, pioneering expert Judah Phillips introduces the concepts, terms, and methods that comprise the science and art of digital analysis for web, site, social, video, and other types of quantitative and qualitative data. Business readers—from new practitioners to experienced executives—who want to understand how digital analytics can be used to reduce costs and increase profitable revenue throughout the business should read this book. Phillips delivers a comprehensive review of the core concepts, vocabulary, and frameworks, including analytical methods and tools that can help you successfully integrate analytical processes, technology, and people into all aspects of business operations. This unbiased and product-independent primer draws from the author's extensive experience doing and managing analytics in this field.
Univariate, Bivariate, and Multivariate Statistics Using R by Daniel J. Denis Pdf
A practical source for performing essential statistical analyses and data management tasks in R Univariate, Bivariate, and Multivariate Statistics Using R offers a practical and very user-friendly introduction to the use of R software that covers a range of statistical methods featured in data analysis and data science. The author— a noted expert in quantitative teaching —has written a quick go-to reference for performing essential statistical analyses and data management tasks in R. Requiring only minimal prior knowledge, the book introduces concepts needed for an immediate yet clear understanding of statistical concepts essential to interpreting software output. The author explores univariate, bivariate, and multivariate statistical methods, as well as select nonparametric tests. Altogether a hands-on manual on the applied statistics and essential R computing capabilities needed to write theses, dissertations, as well as research publications. The book is comprehensive in its coverage of univariate through to multivariate procedures, while serving as a friendly and gentle introduction to R software for the newcomer. This important resource: Offers an introductory, concise guide to the computational tools that are useful for making sense out of data using R statistical software Provides a resource for students and professionals in the social, behavioral, and natural sciences Puts the emphasis on the computational tools used in the discovery of empirical patterns Features a variety of popular statistical analyses and data management tasks that can be immediately and quickly applied as needed to research projects Shows how to apply statistical analysis using R to data sets in order to get started quickly performing essential tasks in data analysis and data science Written for students, professionals, and researchers primarily in the social, behavioral, and natural sciences, Univariate, Bivariate, and Multivariate Statistics Using R offers an easy-to-use guide for performing data analysis fast, with an emphasis on drawing conclusions from empirical observations. The book can also serve as a primary or secondary textbook for courses in data analysis or data science, or others in which quantitative methods are featured.
Adventures in Social Research by Earl Babbie,William E. Wagner, III,Jeanne Zaino Pdf
Proud sponsor of the 2019 SAGE Keith Roberts Teaching Innovations Award—enabling graduate students and early career faculty to attend the annual ASA pre-conference teaching and learning workshop. Recipient of the 2018 Cornerstone Author Award! Inspire students to pursue their own adventures in social research with this practical, hands-on introduction to data conceptualization, measurement, and association through active learning. Adventures in Social Research: Data Analysis Using IBM® SPSS® Statistics offers a practical, hands-on introduction to the logic of social science research for students in many disciplines. The fully revised Tenth Edition offers step-by-step instruction on data analysis using the latest version (24.0) of SPSS and current data from the General Social Survey. Organized to parallel most introductory research methods texts, this text starts with an introduction to computerized data analysis and the social research process, then takes readers step-by-step through univariate, bivariate, and multivariate analysis using SPSS Statistics. The range of topics, from beginning to advanced, make Adventures in Social Research appropriate for both undergraduate and graduate courses. For students who are using SPSS for the first time, the free online study site includes video tutorials on basic procedures and operations and includes all SPSS data sets necessary for completing the exercises in the book. Available with Perusall—an eBook that makes it easier to prepare for class Perusall is an award-winning eBook platform featuring social annotation tools that allow students and instructors to collaboratively mark up and discuss their SAGE textbook. Backed by research and supported by technological innovations developed at Harvard University, this process of learning through collaborative annotation keeps your students engaged and makes teaching easier and more effective. Learn more.
Primer to Analysis of Genomic Data Using R by Cedric Gondro Pdf
Through this book, researchers and students will learn to use R for analysis of large-scale genomic data and how to create routines to automate analytical steps. The philosophy behind the book is to start with real world raw datasets and perform all the analytical steps needed to reach final results. Though theory plays an important role, this is a practical book for graduate and undergraduate courses in bioinformatics and genomic analysis or for use in lab sessions. How to handle and manage high-throughput genomic data, create automated workflows and speed up analyses in R is also taught. A wide range of R packages useful for working with genomic data are illustrated with practical examples. The key topics covered are association studies, genomic prediction, estimation of population genetic parameters and diversity, gene expression analysis, functional annotation of results using publically available databases and how to work efficiently in R with large genomic datasets. Important principles are demonstrated and illustrated through engaging examples which invite the reader to work with the provided datasets. Some methods that are discussed in this volume include: signatures of selection, population parameters (LD, FST, FIS, etc); use of a genomic relationship matrix for population diversity studies; use of SNP data for parentage testing; snpBLUP and gBLUP for genomic prediction. Step-by-step, all the R code required for a genome-wide association study is shown: starting from raw SNP data, how to build databases to handle and manage the data, quality control and filtering measures, association testing and evaluation of results, through to identification and functional annotation of candidate genes. Similarly, gene expression analyses are shown using microarray and RNAseq data. At a time when genomic data is decidedly big, the skills from this book are critical. In recent years R has become the de facto tool for analysis of gene expression data, in addition to its prominent role in analysis of genomic data. Benefits to using R include the integrated development environment for analysis, flexibility and control of the analytic workflow. Included topics are core components of advanced undergraduate and graduate classes in bioinformatics, genomics and statistical genetics. This book is also designed to be used by students in computer science and statistics who want to learn the practical aspects of genomic analysis without delving into algorithmic details. The datasets used throughout the book may be downloaded from the publisher’s website.
The Psychology Research Handbook by Frederick T. L. Leong,James T. Austin Pdf
In the Third Edition of The Psychology Research Handbook editors Frederick T. L. Leong and James T. Austin have assembled experienced expert researchers to provide graduate students and research assistants with a comprehensive framework for conducting many types of psychology research. The book is organized around the idea of a "research script," following the step-by-step process of research planning, design, data collection, analysis, and disseminating research. Many chapters are coauthored by advanced graduate students to give their fellow students a sense of real-world research, adding to the clarity and practicality of many chapters. Students and instructors alike will appreciate chapters on topics typically missing from introductory methods texts, including applying for research grants, dealing with journal editors and reviewers, working within research teams, and conducting cross-cultural research. Structures such as recommended readings and exercises guide students to develop and expand their research skills. New chapters include Power and Evidence, IRB as Critical Collaborators in Research, Alternative Data Collection Strategies, Structural Equation Modeling and Replicability and Reproducibility. A comprehensive, easy-to-understand guide to the entire research process, this book quickly and efficiently equips advanced students and research assistants to conduct a full research project.
Statistical Data Analysis Using SAS by Mervyn G. Marasinghe,Kenneth J. Koehler Pdf
The aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data. The book begins with an introduction beyond the basics of SAS, illustrated with non-trivial, real-world, worked examples. It proceeds to SAS programming and applications, SAS graphics, statistical analysis of regression models, analysis of variance models, analysis of variance with random and mixed effects models, and then takes the discussion beyond regression and analysis of variance to conclude. Pedagogically, the authors introduce theory and methodological basis topic by topic, present a problem as an application, followed by a SAS analysis of the data provided and a discussion of results. The text focuses on applied statistical problems and methods. Key features include: end of chapter exercises, downloadable SAS code and data sets, and advanced material suitable for a second course in applied statistics with every method explained using SAS analysis to illustrate a real-world problem. New to this edition: • Covers SAS v9.2 and incorporates new commands • Uses SAS ODS (output delivery system) for reproduction of tables and graphics output • Presents new commands needed to produce ODS output • All chapters rewritten for clarity • New and updated examples throughout • All SAS outputs are new and updated, including graphics • More exercises and problems • Completely new chapter on analysis of nonlinear and generalized linear models • Completely new appendix Mervyn G. Marasinghe, PhD, is Associate Professor Emeritus of Statistics at Iowa State University, where he has taught courses in statistical methods and statistical computing. Kenneth J. Koehler, PhD, is University Professor of Statistics at Iowa State University, where he teaches courses in statistical methodology at both graduate and undergraduate levels and primarily uses SAS to supplement his teaching.
