Download or read book Analysis of Nominal Data written by H. T. Reynolds and published by . This book was released on 1986 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Using Stata for Quantitative Analysis written by Kyle C. Longest and published by SAGE. This book was released on 2012 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using Stata for Quantitative Analysis is an applied, self-teaching resource. It is written in such a way that a reader with no experience with statistical software can sit down and be working with data in a very short amount of time. The author proposes to teach the language of Stata from an intuitive perspective, furthering students’ overall retention, using many screen shots from Stata to guide students.

Download or read book Learning Statistics with R written by Daniel Navarro and published by Lulu.com. This book was released on 2013-01-13 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com

Download or read book An Introduction to Categorical Data Analysis written by Alan Agresti and published by John Wiley & Sons. This book was released on 2018-10-11 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Elementary Applied Statistics written by Linton C. Freeman and published by New York : Wiley. This book was released on 1965 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book OpenIntro Statistics written by David Diez and published by . This book was released on 2015-07-02 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The OpenIntro project was founded in 2009 to improve the quality and availability of education by producing exceptional books and teaching tools that are free to use and easy to modify. We feature real data whenever possible, and files for the entire textbook are freely available at openintro.org. Visit our website, openintro.org. We provide free videos, statistical software labs, lecture slides, course management tools, and many other helpful resources.

Download or read book Show Me the Proof written by Stephen H. White and published by Lead + Learn Press. This book was released on 2005 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the outcry from parents and citizens everywhere. Educators must be able to provide evidence of improvement, but how? Each person in the educational system must be a part of the assessment team superintendents, principals, teachers, and parents."

Download or read book Implementing Strategies to Enhance Public Health Surveillance of Physical Activity in the United States written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2019-07-19 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: Physical activity has far-reaching benefits for physical, mental, emotional, and social health and well-being for all segments of the population. Despite these documented health benefits and previous efforts to promote physical activity in the U.S. population, most Americans do not meet current public health guidelines for physical activity. Surveillance in public health is the ongoing systematic collection, analysis, and interpretation of outcome-specific data, which can then be used for planning, implementation and evaluation of public health practice. Surveillance of physical activity is a core public health function that is necessary for monitoring population engagement in physical activity, including participation in physical activity initiatives. Surveillance activities are guided by standard protocols and are used to establish baseline data and to track implementation and evaluation of interventions, programs, and policies that aim to increase physical activity. However, physical activity is challenging to assess because it is a complex and multidimensional behavior that varies by type, intensity, setting, motives, and environmental and social influences. The lack of surveillance systems to assess both physical activity behaviors (including walking) and physical activity environments (such as the walkability of communities) is a critical gap. Implementing Strategies to Enhance Public Health Surveillance of Physical Activity in the United States develops strategies that support the implementation of recommended actions to improve national physical activity surveillance. This report also examines and builds upon existing recommended actions.

Download or read book Unlocking the Power of Data A Beginner s Guide to Data Analysis written by Balasubramanian Thiagarajan and published by Otolaryngology online. This book was released on 2023-06-13 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Welcome to the world of data analysis! In today's data-driven era, the ability to effectively analyze and derive insights from data has become a vital skill for individuals and organizations across various domains. This book aims to serve as your comprehensive guide to understanding and performing data analysis, from the fundamental concepts to the practical applications. Chapter 1 introduces you to the fascinating realm of data analysis. We delve into the importance of data analysis in decision-making processes and highlight its role in gaining valuable insights and making informed choices. Understanding the power of data analysis sets the foundation for your journey ahead. Chapter 2 focuses on data entry, a crucial step in the data analysis process. We explore different methods and techniques for entering data accurately, ensuring the reliability and integrity of your dataset. Effective data entry practices are essential for obtaining meaningful results. In Chapter 3, we explore the different types of data analysis. Whether it's exploratory, descriptive, diagnostic, predictive, or prescriptive analysis, you will gain an understanding of each type and when to employ them in various scenarios. This chapter equips you with the knowledge to choose the appropriate analysis technique for your specific needs. To lay the groundwork for your data analysis journey, Chapter 4 familiarizes you with the basic terminology commonly used in the field. From variables and observations to measures of central tendency and variability, this chapter ensures you have a solid grasp of the foundational concepts necessary for effective data analysis. Chapter 5 focuses on setting up your data analysis environment. We guide you through the process of installing the necessary software and configuring your data workspace. Creating an optimal environment is crucial for seamless and efficient data analysis. Data preprocessing takes center stage in Chapter 6. We delve into the essential steps of data cleaning, transformation, and handling missing values. By mastering these techniques, you will be able to prepare your data for analysis, ensuring its quality and usability. In Chapter 7, we explore the exciting world of data exploration and visualization. Understanding the distribution of data and identifying relationships between variables are key aspects of uncovering meaningful insights. We delve into creating various charts and graphs to visually represent data, aiding in its interpretation and analysis. Chapter 8 introduces you to statistical analysis techniques. Descriptive statistics help us summarize and describe data, while inferential statistics enable us to make inferences and draw conclusions about populations based on sample data. Additionally, hypothesis testing allows us to validate our assumptions and test specific predictions. Predictive analytics takes the spotlight in Chapter 9. We explore techniques such as linear and logistic regression, decision trees, and clustering algorithms. These techniques empower you to make predictions and forecasts based on historical data, providing valuable insights for decision-making. Chapter 10 is dedicated to machine learning, an exciting field within data analysis. We introduce the fundamentals of machine learning, including supervised and unsupervised learning algorithms. Understanding these concepts opens doors to more advanced data analysis techniques and applications. Ethics in data analysis takes center stage in Chapter 11. We delve into the critical considerations of privacy concerns, data bias, and fairness in data analysis. Ethical data practices are crucial to ensure the responsible and ethical use of data in analysis. Chapter 12 explores the wide-ranging applications of data analysis. We delve into the domains of business analytics, healthcare analytics, sports analytics, and social media analytics, highlighting how data analysis drives insights and informs decision-making in these fields. Finally, Chapter 13 serves as a conclusion and sets you on the path for further learning and development. We recap the key concepts covered in the book, provide tips for advancing your data analysis skills, and discuss future trends and innovations in the field. We hope this book serves as a valuable resource in your data analysis journey. Whether you are a student, professional, or data enthusiast, we believe that understanding and applying data analysis.

Download or read book Applied Factor Analysis written by Rudolf J. Rummel and published by Northwestern University Press. This book was released on 1988 with total page 644 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied Factor Analysis was written to help others apply factor analysis throughout the sciences with the conviction that factor analysis is a calculus of the social sciences. The book developed from research undertaken to do a 236-variable cross-national analysis.

Download or read book Applied Missing Data Analysis written by Craig K. Enders and published by Guilford Press. This book was released on 2010-04-23 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: Walking readers step by step through complex concepts, this book translates missing data techniques into something that applied researchers and graduate students can understand and utilize in their own research. Enders explains the rationale and procedural details for maximum likelihood estimation, Bayesian estimation, multiple imputation, and models for handling missing not at random (MNAR) data. Easy-to-follow examples and small simulated data sets illustrate the techniques and clarify the underlying principles. The companion website includes data files and syntax for the examples in the book as well as up-to-date information on software. The book is accessible to substantive researchers while providing a level of detail that will satisfy quantitative specialists. This book will appeal to researchers and graduate students in psychology, education, management, family studies, public health, sociology, and political science. It will also serve as a supplemental text for doctoral-level courses or seminars in advanced quantitative methods, survey analysis, longitudinal data analysis, and multilevel modeling, and as a primary text for doctoral-level courses or seminars in missing data.

Download or read book Statistical Methods written by Rudolf J. Freund and published by Elsevier. This book was released on 2003-01-07 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt: This broad text provides a complete overview of most standard statistical methods, including multiple regression, analysis of variance, experimental design, and sampling techniques. Assuming a background of only two years of high school algebra, this book teaches intelligent data analysis and covers the principles of good data collection. * Provides a complete discussion of analysis of data including estimation, diagnostics, and remedial actions * Examples contain graphical illustration for ease of interpretation * Intended for use with almost any statistical software * Examples are worked to a logical conclusion, including interpretation of results * A complete Instructor's Manual is available to adopters

Download or read book Handbook of Statistical Analysis and Data Mining Applications written by Ken Yale and published by Elsevier. This book was released on 2017-11-09 with total page 824 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. - Includes input by practitioners for practitioners - Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models - Contains practical advice from successful real-world implementations - Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions - Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications

Download or read book The SAGE Handbook of Regression Analysis and Causal Inference written by Henning Best and published by SAGE. This book was released on 2013-12-20 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: ′The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.′ - John Fox, Professor, Department of Sociology, McMaster University ′The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.′ - Ben Jann, Executive Director, Institute of Sociology, University of Bern ′Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.′ -Tom Smith, Senior Fellow, NORC, University of Chicago Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities. Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method’s logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method’s application, making this an ideal text for PhD students and researchers embarking on their own data analysis.

Download or read book An Introduction to Statistics and Data Analysis Using Stata written by Lisa Daniels and published by SAGE Publications. This book was released on 2019-01-11 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Statistics and Data Analysis Using Stata® by Lisa Daniels and Nicholas Minot provides a step-by-step introduction for statistics, data analysis, or research methods classes with Stata. Concise descriptions emphasize the concepts behind statistics for students rather than the derivations of the formulas. With real-world examples from a variety of disciplines and extensive detail on the commands in Stata, this text provides an integrated approach to research design, statistical analysis, and report writing for social science students.

Download or read book Handbook of Polytomous Item Response Theory Models written by Michael Nering and published by Taylor & Francis. This book was released on 2011-01-19 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive Handbook focuses on the most used polytomous item response theory (IRT) models. These models help us understand the interaction between examinees and test questions where the questions have various response categories. The book reviews all of the major models and includes discussions about how and where the models originated, conceptually and in practical terms. Diverse perspectives on how these models can best be evaluated are also provided. Practical applications provide a realistic account of the issues practitioners face using these models. Disparate elements of the book are linked through editorial sidebars that connect common ideas across chapters, compare and reconcile differences in terminology, and explain variations in mathematical notation. These sidebars help to demonstrate the commonalities that exist across the field. By assembling this critical information, the editors hope to inspire others to use polytomous IRT models in their own research so they too can achieve the type of improved measurement that such models can provide. Part 1 examines the most commonly used polytomous IRT models, major issues that cut across these models, and a common notation for calculating functions for each model. An introduction to IRT software is also provided. Part 2 features distinct approaches to evaluating the effectiveness of polytomous IRT models in various measurement contexts. These chapters appraise evaluation procedures and fit tests and demonstrate how to implement these procedures using IRT software. The final section features groundbreaking applications. Here the goal is to provide solutions to technical problems to allow for the most effective use of these models in measuring educational, psychological, and social science abilities and traits. This section also addresses the major issues encountered when using polytomous IRT models in computerized adaptive testing. Equating test scores across different testing contexts is the focus of the last chapter. The various contexts include personality research, motor performance, health and quality of life indicators, attitudes, and educational achievement. Featuring contributions from the leading authorities, this handbook will appeal to measurement researchers, practitioners, and students who want to apply polytomous IRT models to their own research. It will be of particular interest to education and psychology assessment specialists who develop and use tests and measures in their work, especially researchers in clinical, educational, personality, social, and health psychology. This book also serves as a supplementary text in graduate courses on educational measurement, psychometrics, or item response theory.

Download or read book Data Science Live Book written by Pablo Casas and published by . This book was released on 2018-03-16 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a practical guide to problems that commonly arise when developing a machine learning project. The book's topics are: Exploratory data analysis Data Preparation Selecting best variables Assessing Model Performance More information on predictive modeling will be included soon. This book tries to demonstrate what it says with short and well-explained examples. This is valid for both theoretical and practical aspects (through comments in the code). This book, as well as the development of a data project, is not linear. The chapters are related among them. For example, the missing values chapter can lead to the cardinality reduction in categorical variables. Or you can read the data type chapter and then change the way you deal with missing values. You¿ll find references to other websites so you can expand your study, this book is just another step in the learning journey. It's open-source and can be found at http://livebook.datascienceheroes.com