*Mathematics | Program Guide: Data Analysis and Presentation

There are a variety of statistical techniques used to analyse quantitative data that masters students, advanced undergraduates and researchers in the social sciences are expected to be able to understand and undertake. This book explains these techniques, when it is appropriate to use them, how to carry them out and how to write up the results. Most books which describe these techniques do so at too advanced or technical a level to be readily understood by many students who need to use them. In contrast the following features characterise this book: . - concise and accessible introduction to calculating and interpreting advanced statistical techniques. - use of a small data set of simple numbers specifically designed to illustrate the nature and manual calculation of the most important statistics in each technique. - succinct illustration of writing up the results of these analyses. - minimum of mathematical, statistical and technical notation. - annotated bibliography and glossary of key concepts. Commonly used software is introduced, and instructions are presented for carrying out analyses and interpreting the output using the computer programs of SPSS Release 11 for Windows and a version of LISREL 8.51, which is freely available online. Designed as a textbook for postgraduate and advanced undergraduate courses across the socio-behavioural sciences, this book will also serve as a personal reference for researchers in disciplines such as sociology and psychology

In this monograph, optimality is attained for nontrivially sized problems via the branch-and-bound paradigm.For many combinatorial problems, branch-and-bound approaches have been proposed and/or developed. However, until now, there has not been a single resource in statistical data analysis to summarize and illustrate available methods for applying the branch-and-bound process. This monograph provides clear explanatory text, illustrative mathematics and algorithms, demonstrations of the iterative process, psuedocode, and well-developed examples for applications of the branch-and-bound paradigm to important problems in combinatorial data analysis. Supplementary material, such as computer programs, are provided on the world wide web.

This book presents the history and development of compositional data analysis along with Aitchison's log-ratio approach. Compositional Data Analysis describes the state of the art both in theoretical fields as well as applications in the different fields of science. Applied scientists working on compositional data analysis in any field of science, both in academia and professionals will benefit from this book, along with graduate students in any field of science working with compositional data.

This is the first book that provides a comprehensive treatment of the full range of algorithms available for conceptual data analysis, spanning creation, maintenance, display and manipulation of concept lattices. The accompanying website allows you to gain a greater understanding of the principles covered in the book through actively working on the topics discussed. The three main areas explored are interactive mining of documents or collections of documents (including Web documents), automatic text ranking, and rule mining from structured data. The potentials of conceptual data analysis in the application areas being considered are further illustrated by two detailed case studies.

The first part of this book is devoted to methods seeking relevant dimensions of data. The variables thus obtained provide a synthetic description which often results in a graphical representation of the data. After a general presentation of the discriminating analysis, the second part is devoted to clustering methods which constitute another method, often complementary to the methods described in the first part, to synthesize and to analyze the data. The book concludes by examining the links existing between data mining and data analysis.

Statistics lectures have been a source of much bewilderment and frustration for generations of students. This book attempts to remedy the situation by expounding a logical and unified approach to the whole subject of data analysis. This text is intended as a tutorial guide for senior undergraduates and research students in science and engineering. After explaining the basic principles of Bayesian probability theory, their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing. Other topics covered include reliability analysis, multivariate optimization, least-squares and maximum likelihood, error-propagation, hypothesis testing, maximum entropy and experimental design.

The volume presents recent advances in data analysis and decision support and gives an actual overview on the interface between mathematics, operations research, statistics, computer science, and management science. Areas that receive considerable attention in the book are discrimination and clustering, multidimensional scaling, data mining and decision support systems as well as applications in marketing and corporate planning. The reader will find material on recent technical and methodological developments and a large number of applications demonstrating the usefulness of the newly developed techniques.

The preferred method of data analysis of quantitative experiments is the method of least squares. Often, however, the full power of the method is overlooked and very few books deal with this subject at the level that it deserves. The purpose of this book is to fill this gap and include the type of information required to help scientists and engineers apply the method to problems in their special fields of interest. In addition, graduate students in science and engineering doing work of experimental nature can benefit from this book. Particularly, both linear and non-linear least squares, the use of experimental error estimates for data weighting, procedures to include prior estimates, methodology for selecting and testing models, prediction analysis, and some non-parametric methods are discussed.

Poor data quality is known to compromise the credibility and efficiency of commercial and public endeavours. Also, the importance of managing data quality has increased manifold as the diversity of sources, formats and volume of data grows. This volume targets the data quality in the light of collaborative information systems where data creation and ownership is increasingly difficult to establish.

This book presents the full formalization of Geometric Data Analysis (GDA) in terms of linear algebra - the most original and far-reaching consequential feature of the approach - and shows also how to integrate the standard statistical tools such as Analysis of Variance, including Bayesian methods. Chapter 9, Research Case Studies, is nearly a book in itself; it presents the methodology in action on three extensive applications, one for medicine, one from political science, and one from education. Thus the readership of the book concerns both mathematicians interested in the applications of mathematics, and researchers willing to master an exceptionally powerful approach of statistical data analysis.

Graphical models are of increasing importance in applied statistics, and in particular in data mining. Providing a self-contained introduction and overview to learning relational, probabilistic, and possibilistic networks from data, this second edition of Graphical Models is thoroughly updated to include the latest research in this burgeoning field, including a new chapter on visualization. The text provides graduate students, and researchers with all the necessary background material, including modelling under uncertainty, decomposition of distributions, graphical representation of distributions, and applications relating to graphical models and problems for further research.

This book focuses on models and data that arise from repeated observations of a cross-section of individuals, households or companies. These models have found important applications within business, economics, education, political science and other social science disciplines. The author introduces the foundations of longitudinal and panel data analysis at a level suitable for quantitatively oriented graduate social science students as well as individual researchers.

BIRT is an Eclipse-based open source reporting system for web applications based on Java and Java EE. To address a wide range of reporting needs within a typical application, ranging from operational or enterprise reporting to multi-dimensional online analytical processing (OLAP), you need to know BIRT from head to toe. If you wish to start making reports easily and quickly, and also want to be up-to-date with the latest developments in BIRT, then this book is for you. It will guide you from scratch to develop reports using the Eclipse BIRT project. You will learn how to connect to data, use report items to display and format data, and use scripting to build advanced reports and charts.The book steers you through each step of report setup, to creating, designing, formatting, and deploying reports with data from a wide range of data sources.

Basic theoretical concepts of statistics as well as sampling methods are often misinterpreted by students and less experienced researchers. This book addresses this issue by providing a hands-on practical guide to conducting data analysis using EViews combined with a variety of illustrative models (and their extensions). Models having numerically dependent variables based on a cross-section data set (such as univariate, multivariate and nonlinear models as well as non-parametric regressions) are concentrated on. Contains 200 examples with special notes and comments based on the author's own empirical findings as well as over 400 illustrative outputs of regressions from EViews. Comes with supplementary material, including work-files containing selected equation and system specifications that have been applied in the book.

BIRT, which stands for Business Intelligence and Reporting Tools, is an Eclipse-based open-source reporting system for Java and J2EE based web applications. Including the word "Tools" in the acronym is appropriate, since BIRT is in fact a collection of development tools and technologies used for developing reports utilizing the BIRT runtime framework component on your application server. BIRT isn't essentially a product, but a series of core technologies on top of which products and solutions are built, similar in fashion to the Eclipse framework.This book has a fast-paced, task-driven, tutorial style, which provides understanding and structure, not just lists of steps to follow. The focus is on the most visible and familiar product built with the BIRT framework, which is the BIRT Report Designer. The BIRT Report Designer is an Eclipse plug-in that utilizes BIRT technologies to allow users to design reports in the BIRT document format. Also covered is the BIRT charting engine, which lets you add charts to your application.

This accessible book is essential reading for those looking for a short and simple guide to basic data analysis. Written for the complete beginner, the book is the ideal companion when undertaking quantitative data analysis for the first time using SPSS. The book uses a simple example of quantitative data analysis that would be typical to the health field to take you through the process of data analysis step by step. Handy screenshots illustrate each step of the process so you can try out the analysis for yourself, and apply it to your own research with ease. Quantitative Data Analysis Using SPSS is helpful for any students in health and social sciences with little or no experience of quantitative data analysis and statistics.

Because of its unique visual emphasis, Visual Six Sigma opens the doors for you to take an active role in data-driven decision making, empowering you to leverage your contextual knowledge to pose relevant questions and make sound decisions. This book shows you how to leverage dynamic visualization and exploratory data analysis techniques to: See the sources of variation in your data Search for clues in your data to construct hypotheses about underlying behavior Identify key drivers and models Shape and build your own real-world Six Sigma experience Whether you work involves a Six Sigma improvement project, a design project, a data-mining inquiry, or a scientific study, this practical breakthrough guide equips you with the strategies, process, and road map to put Visual Six Sigma to work for your company. Broaden and deepen your implementation of Visual Six Sigma with the intuitive and easy-to-use tools found in Visual Six Sigma: Making Data Analysis Lean.