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In a world in which we are constantly surrounded by data, figures, and statistics, it is imperative to understand and to be able to use quantitative methods. Statistical models and methods are among the most important tools in economic analysis, decision-making and business planning. This textbook, “Exploratory Data Analysis in Business and Economics”, aims to familiarise students of economics and business as well as practitioners in firms with the basic principles, techniques, and applications of descriptive statistics and data analysis. Drawing on practical examples from business settings, it demonstrates the basic descriptive methods of univariate and bivariate analysis. The textbook covers a range of subject matter, from data collection and scaling to the presentation and univariate analysis of quantitative data, and also includes analytic procedures for assessing bivariate relationships. It does not confine itself to presenting descriptive statistics, but also addresses the use of computer programmes such as Excel, SPSS, and STATA, thus treating all of the topics typically covered in a university course on descriptive statistics. The German edition of this textbook is one of the “bestsellers” on the German market for literature in statistics.

Embroidered faux leather lined jacket Stud details Interior pouch pocket Sleeve zip vents

Embroidered faux leather lined jacket Stud details Interior pouch pocket Sleeve zip vents

Embroidered faux leather lined jacket Stud details Interior pouch pocket Sleeve zip vents

Embroidered faux leather lined jacket Stud details Interior pouch pocket Sleeve zip vents

Guided by the precept that to understand any phenomenon well, it is necessary to start by looking at it in broad, nonspecialized terms, Robert A Stebbins takes the reader through the process of exploratory research in an easy-to-read style providing the student or researcher with a complete reference for carrying out this type of research. 

Acceptable/Fair condition. Book is worn, but the pages are complete, and the text is legible. Has wear to binding and pages, may be ex-library. 1.81

Since the publication of the bestselling first edition, many advances have been made in exploratory data analysis (EDA). Covering innovative approaches for dimensionality reduction, clustering, and visualization, Exploratory Data Analysis with MATLAB®, Second Edition uses numerous examples and applications to show how the methods are used in practice. New to the Second Edition Discussions of nonnegative matrix factorization, linear discriminant analysis, curvilinear component analysis, independent component analysis, and smoothing splines An expanded set of methods for estimating the intrinsic dimensionality of a data set Several clustering methods, including probabilistic latent semantic analysis and spectral-based clustering Additional visualization methods, such as a rangefinder boxplot, scatterplots with marginal histograms, biplots, and a new method called Andrews’ images Instructions on a free MATLAB GUI toolbox for EDA Like its predecessor, this edition continues to focus on using EDA methods, rather than theoretical aspects. The MATLAB codes for the examples, EDA toolboxes, data sets, and color versions of all figures are available for download at http://pi-sigma.info

This volume presents the important concepts required for implementing two disciplines of factor analysis - exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) - with an emphasis on EFA-CFA linkages. Modern extensions of older data analysis methods (e.g. analysis of variance, regression, multivariate analysis of variance, and descriptive discriminant analysis) have brought theory-testing procedures to the analytic forefornt. Variations of factor analysis, such as the factoring of people or time, have great potential to inform psychological research.Instructors or students who seek a clear and concise test about factor analysis will find this book to be an invaluable resource.

This title is part of the Pearson Modern Classics series. Pearson Modern Classics are acclaimed titles at a value price. Please visit  www.pearson.com/statistics-classics-series  for a complete list of titles. The approach in this introductory book is that of informal study of the data. Methods range from plotting picture-drawing techniques to rather elaborate numerical summaries. Several of the methods are the original creations of the author, and all can be carried out either with pencil or aided by hand-held calculator. 0134995457 / 9780134995458 EXPLORATORY DATA ANALYSIS (CLASSIC VERSION), 1/e

The approach in this introductory book is that of informal study of the data. Methods range from plotting picture-drawing techniques to rather elaborate numerical summaries. Several of the methods are the original creations of the author, and all can be carried out either with pencil or aided by hand-held calculator.

The nineteenth century was a formative period for electromagnetism and electrodynamics. Hans Christian Ørsted’s groundbreaking discovery of the interaction between electricity and magnetism in 1820 inspired a wave of research, led to the science of electrodynamics, and resulted in the development of electromagnetic theory. Remarkably, in response, André-Marie Ampère and Michael Faraday developed two incompatible, competing theories. Although their approaches and conceptual frameworks were fundamentally different, together their work launched a technological revolution—laying the foundation for our modern scientific understanding of electricity—and one of the most important debates in physics, between electrodynamic action-at-a-distance and field theories. In this foundational study, Friedrich Steinle compares the influential work of Ampère and Faraday to reveal the prominent role of exploratory experimentation in the development of science. While this exploratory phase was responsible for decisive conceptual innovations, it has yet to be examined in such great detail. Focusing on Ampère’s and Faraday’s research practices, reconstructed from previously unknown archival materials, including laboratory notes, diaries, letters, and interactions with instrument makers, this book considers both the historic and epistemological basis of exploratory experimentation and its importance to scientific development.Winner of the 2017 Ungar German Translations Award from the American Translators Association

Exploratory Factor Analysis (EFA) has played a major role in research conducted in the social sciences for more than 100 years, dating back to the pioneering work of Spearman on mental abilities. Since that time, EFA has become one of the most commonly used quantitative methods in many of the social sciences, including psychology, business, sociology, education, political science, and communications. To a lesser extent, it has also been utilized within the physical and biological sciences. Despite its long and widespread usage in many domains, numerous aspects of the underlying theory and application of EFA are poorly understood by researchers. Indeed, perhaps no widely used quantitative method requires more decisions on the part of a researcher and offers as wide an array of procedural options as EFA does. This book provides a non-mathematical introduction to the underlying theory of EFA and reviews the key decisions that must be made in its implementation. Among the issues discussed are the use of confirmatory versus exploratory factor analysis, the use of principal components analysis versus common factor analysis, procedures for determining the appropriate number of factors, and methods for rotating factor solutions. Explanations and illustrations of the application of different factor analytic procedures are provided for analyses using common statistical packages (SPSS and SAS), as well as a free package available on the web (Comprehensive Exploratory Factor Analysis). In addition, practical instructions are provided for conducting a number of useful factor analytic procedures not included in the statistical packages.

Book is in NEW condition. 1.81

This title is part of the Pearson Modern Classics series. Pearson Modern Classics are acclaimed titles at a value price. Please visit  www.pearson.com/statistics-classics-series  for a complete list of titles. The approach in this introductory book is that of informal study of the data. Methods range from plotting picture-drawing techniques to rather elaborate numerical summaries. Several of the methods are the original creations of the author, and all can be carried out either with pencil or aided by hand-held calculator. 0134995457 / 9780134995458 EXPLORATORY DATA ANALYSIS (CLASSIC VERSION), 1/e

A book for anyone who wants to learn programming to explore and create, with exercises and projects to help the reader learn by doing.This book introduces programming to readers with a background in the arts and humanities; there are no prerequisites, and no knowledge of computation is assumed. In it, Nick Montfort reveals programming to be not merely a technical exercise within given constraints but a tool for sketching, brainstorming, and inquiring about important topics. He emphasizes programming's exploratory potential―its facility to create new kinds of artworks and to probe data for new ideas. The book is designed to be read alongside the computer, allowing readers to program while making their way through the chapters. It offers practical exercises in writing and modifying code, beginning on a small scale and increasing in substance. In some cases, a specification is given for a program, but the core activities are a series of “free projects,” intentionally underspecified exercises that leave room for readers to determine their own direction and write different sorts of programs. Throughout the book, Montfort also considers how computation and programming are culturally situated―how programming relates to the methods and questions of the arts and humanities. The book uses Python and Processing, both of which are free software, as the primary programming languages.

2nd Edition No-pa16apr2015-kap

The nineteenth century was a formative period for electromagnetism and electrodynamics. Hans Christian Ørsted’s groundbreaking discovery of the interaction between electricity and magnetism in 1820 inspired a wave of research, led to the science of electrodynamics, and resulted in the development of electromagnetic theory. Remarkably, in response, André-Marie Ampère and Michael Faraday developed two incompatible, competing theories. Although their approaches and conceptual frameworks were fundamentally different, together their work launched a technological revolution—laying the foundation for our modern scientific understanding of electricity—and one of the most important debates in physics, between electrodynamic action-at-a-distance and field theories. In this foundational study, Friedrich Steinle compares the influential work of Ampère and Faraday to reveal the prominent role of exploratory experimentation in the development of science. While this exploratory phase was responsible for decisive conceptual innovations, it has yet to be examined in such great detail. Focusing on Ampère’s and Faraday’s research practices, reconstructed from previously unknown archival materials, including laboratory notes, diaries, letters, and interactions with instrument makers, this book considers both the historic and epistemological basis of exploratory experimentation and its importance to scientific development.Winner of the 2017 Ungar German Translations Award from the American Translators Association

The approach in this introductory book is that of informal study of the data. Methods range from plotting picture-drawing techniques to rather elaborate numerical summaries. Several of the methods are the original creations of the author, and all can be carried out either with pencil or aided by hand-held calculator.

A firm knowledge of factor analysis is key to understanding much published research in the social and behavioral sciences. Exploratory Factor Analysis by W. Holmes Finch provides a solid foundation in exploratory factor analysis (EFA), which along with confirmatory factor analysis, represents one of the two major strands in this field. The book lays out the mathematical foundations of EFA; explores the range of methods for extracting the initial factor structure; explains factor rotation; and outlines the methods for determining the number of factors to retain in EFA. The concluding chapter addresses a number of other key issues in EFA, such as determining the appropriate sample size for a given research problem, and the handling of missing data.  It also offers brief introductions to exploratory structural equation modeling, and multilevel models for EFA. Example computer code, and the annotated output for all of the examples included in the text are available on an accompanying website.