Spatial Data Analysis

This book presents the theory of capital utilization, a discussion of the econometrics of capital utilization, and econometric tests of the theory using international data. Capital utilization, defined as the proportion of time that capital is working productively, is mainly affected by shift-working. Capital utilization is an important economic variable that has received serious attention from economists only since the mid-1960s In the first part, the authors provide a synthesis of current knowledge, combining a consistent statement of existing theory with some major extensions. In the second part, they turn to the econometrics, first discussing the appropriate methodology and then testing the theory on data from several countries. This empirical work is considerably more sophisticated than previous studies on this topic. Having established the theory and tested it, they move on to consider policy, the relationship between capital utilization and economic growth, and the place of shif

This book provides a practical, comprehensive and up-to-date overview of the use of spatial statistics in epidemiology - the study of the incidence and distribution of diseases. Used appropriately, sp

Unlike other books on the modeling and analysis of experimental data, Design and Analysis of Experiments: Classical and Regression Approaches with SAS not only covers classical experimental design the

Data analysis and machine learning are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can b

Information systems development is not merely a technical intervention but involves social and ethical dilemmas that affect the human, social and organizational domains. To demonstrate this point, the authors conduct a thorough and substantive description and analysis of the conceptual and philosophical underpinnings of systems development. In particular they analyse a number of systems development methodologies including structured methods, prototyping, ETHICS and Soft Systems Methodology to reveal the underlying conceptual and philosophical foundations. The book provides an in-depth analysis of data modelling theory and its links with theories of language and cognition. It offers a framework to describe and analyse different systems development approaches and to explain their strengths and weaknesses. The book is aimed at graduate students taking courses in information systems and data modelling, but will also appeal to information systems managers and professionals for whom the summ

Visualizing the data is an essential part of any data analysis. Modern computing developments have led to big improvements in graphic capabilities and there are many new possibilities for data display

R is rapidly growing in popularity as the environment of choice for data analysis and graphics both in academia and industry. Lattice brings the proven design of Trellis graphics (originally develope

This practical guide to survival data and its analysis for readers with a minimal background in statistics shows why the analytic methods work and how to effectively analyze and interpret epidemiologic and medical survival data with the help of modern computer systems. The introduction presents a review of a variety of statistical methods that are not only key elements of survival analysis but are also central to statistical analysis in general. Techniques such as statistical tests, transformations, confidence intervals, and analytic modeling are presented in the context of survival data but are, in fact, statistical tools that apply to understanding the analysis of many kinds of data. Similarly, discussions of such statistical concepts as bias, confounding, independence, and interaction are presented in the context of survival analysis and also are basic components of a broad range of applications. These topics make up essentially a 'second-year', one-semester biostatistics course in

This practical guide to survival data and its analysis for readers with a minimal background in statistics shows why the analytic methods work and how to effectively analyze and interpret epidemiologic and medical survival data with the help of modern computer systems. The introduction presents a review of a variety of statistical methods that are not only key elements of survival analysis but are also central to statistical analysis in general. Techniques such as statistical tests, transformations, confidence intervals, and analytic modeling are presented in the context of survival data but are, in fact, statistical tools that apply to understanding the analysis of many kinds of data. Similarly, discussions of such statistical concepts as bias, confounding, independence, and interaction are presented in the context of survival analysis and also are basic components of a broad range of applications. These topics make up essentially a 'second-year', one-semester biostatistics course in

This set contains: 9780471469445 A Step-by-Step Approach to Using SAS for Univariate & Multivariate Statistics, Second Edition by Norm O'Rourke, Larry Hatcher, Edward J. Stepanski, SAS Institute I

In this book, top specialists address theoretical, methodological, and empirical multilevel models as they relate to the analysis of individual and cultural data. Divided into four parts, the book ope

本書內容包含1. Introduction to Systems Analysis and Design2. Analyzing the Business Care3. Requirements Modeling4. Data and Process Modeling5. Object Modeling6. Development Strategies7. Output and User Inte

This facilitator's guide helps trainers build educators' understanding of data analysis, promote an inquiry "habit of mind," and develop leaders' capacity to support an inquiry process.

Statistical analysis of extreme data is vital to many disciplines including hydrology, insurance, finance, engineering and environmental sciences. This book provides a self-contained introduction to p

Elden (numerical analysis, Linkoping U.) uses as examples handwritten digits, text summarization, pagerank computations made famous by a certain very popular search engine (the name of which is now us

This book presents models and statistical methods for the analysis of recurrent event data. The authors provide broad, detailed coverage of the major approaches to analysis, while emphasizing the mode

Equity Analysis and Valuation with eVal closes the gap between the theoretical treatment of equity valuation and the actual practice of valuing a company using real-world data. While the underlying th

Secondary data play an increasingly important role in epidemiology and public health research and practice; examples of secondary data sources include national surveys such as the BRFSS and NHIS, claims data for the Medicare and Medicaid systems, and public vital statistics records. Although a wealth of secondary data is available, it is not always easy to locate and access appropriate data to address a research or policy question. This practical guide circumvents these difficulties by providing an introduction to secondary data and issues specific to its management and analysis, followed by an enumeration of major sources of secondary data in the United States. Entries for each data source include the principal focus of the data, years for which it is available, history and methodology of the data collection process, and information about how to access the data and supporting materials, including relevant details about file structure and format.

Authored by a panel of experts in the field, this book focuses on hyperspectral image analysis, systems, and applications. With discussion of application-based projects and case studies, this professi

This book presents a new method for assessing spatial pattern in raster land cover maps based on satellite imagery in a way that incorporates multiple pixel resolutions. This is combined with more co

This book presents state-of-the-art analytical methods from statistics and data mining for the analysis of high-throughput data from genomics and proteomics. It adopts an approach focusing on concept

There is a growing need for a more automated system of partitioning data sets into groups, or clusters. For example, digital libraries and the World Wide Web continue to grow exponentially, the ability to find useful information increasingly depends on the indexing infrastructure or search engine. Clustering techniques can be used to discover natural groups in data sets and to identify abstract structures that might reside there, without having any background knowledge of the characteristics of the data. Clustering has been used in a variety of areas, including computer vision, VLSI design, data mining, bio-informatics (gene expression analysis), and information retrieval, to name just a few. This book focuses on a few of the most important clustering algorithms, providing a detailed account of these major models in an information retrieval context. The beginning chapters introduce the classic algorithms in detail, while the later chapters describe clustering through divergences and sh

Now that it is possible to identify, extract, isolate, and concentrate potential flavor-important chemicals from complex food systems, sensory scientist only have to understand how their chemical data

Spatial patterns of movement are fundamental to the ecology of animal populations, influencing their social organization, mating systems, demography, and the spatial distribution of prey and competito

Remote sensing and GIS are increasingly used as tools for monitoring and managing forests. Remotely sensed and GIS data are now the data sources of choice for capturing, documenting, and understanding

A visually intuitive approach to statistical data analysis Visual Statistics brings the most complex and advanced statistical methods within reach of those with little statistical training by

This reader-friendly second edition provides step-by-step procedures for using data to facilitate effective decision making and focuses on strengthening educators' problem analysis and data interpr

Two professors from the Norwegian University of Science and Technology present different techniques for analyzing business and industrial problems, collecting problem cause data, and eliminating the r

Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a prepro

Foliated spaces look locally like products, but their global structure is generally not a product, and tangential differential operators are correspondingly more complex. In the 1980s, Alain Connes founded what is now known as noncommutative geometry and topology. One of the first results was his generalization of the Atiyah-Singer index theorem to compute the analytic index associated with a tangential (pseudo) - differential operator and an invariant transverse measure on a foliated manifold, in terms of topological data on the manifold and the operator. This second edition presents a complete proof of this beautiful result, generalized to foliated spaces (not just manifolds). It includes the necessary background from analysis, geometry, and topology. The present edition has improved exposition, an updated bibliography, an index, and additional material covering developments and applications since the first edition came out, including the confirmation of the Gap Labeling Conjecture

This volume collects seven of Marc Nerlove's previously published, classic essays on panel data econometrics written over the past thirty-five years, together with a cogent essay on the history of the subject, which began with George Biddell Airey's monograph published in 1861. Since Professor Nerlove's 1966 Econometrica paper with Pietro Balestra, panel data and methods of econometric analysis appropriate to such data have become increasingly important in the discipline. The principal factors in the research environment affecting the future course of panel data econometrics are the phenomenal growth in the computational power available to the individual researcher at his or her desktop and the ready availability of data sets, both large and small, via the Internet. The best way to formulate statistical models for inference is motivated and shaped by substantive problems and understanding of the processes generating the data at hand to resolve them. The essays illustrate both the role

Flow cytometry is now well established in research laboratories and is gaining increasing use in clinical medicine and pathology. The technique enables multiple simultaneous light scatter and fluorescence measurements to be made at the individual cell level at very rapid rates and results in very large quantities of data being collected. Data, however, is just a series of numbers which have to be converted to information which, in turn, must be shown to have meaning. This is the most important single aspect of flow cytometry but it has received relatively little attention. One of the frequently voiced advantages of the technology is that it produces 'good statistics' because large numbers of cells have been analysed. However, it is not very often that confidence limits are placed on results, hence the reader has little or no feel for the inherent variability in the information produced. This book covers very basic number handling techniques, regression analysis, probability functions,

The linear mixed model has become the main parametric tool for the analysis of continuous longitudinal data, as the authors discussed in their 2000 book. Without putting too much emphasis on software,

Thinking Syntactically: A Guide to Argumentation and Analysis is a textbook designed to teach introductory students the skills of relating data to theory and theory to data. Helps students develop the

Thinking Syntactically: A Guide to Argumentation and Analysis is a textbook designed to teach introductory students the skills of relating data to theory and theory to data. Helps students develop the

What can emission lines tell us about an astrophysical object? A workshop at the Space Telescope Science Institute was dedicated to address just this question – for a host of objects (including planetary nebulae and active galactic nuclei) across a broad range of wavelengths (from the infrared through to gamma-rays). Thirteen review articles from internationally renowned experts are presented in this volume. They provide an edited and coherent overview of the latest technical data, techniques in and applications of the study of emission lines from a variety of objects. Chapters include the theory of radiative transfer, photoionising shocks, and emission lines from stellar winds, as well as useful summaries of abundance determinations, atomic data, and diagnostics for IR, UV, gamma-ray and molecular lines. Together these review articles provide an overview of the analysis of emission lines. They summarise current knowledge, highlight outstanding problems and provide focus for fruitful f

The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data

This is the first Visual Basic.NET book to provide a comprehensive discussion of the major data structures and algorithms. Here, instead of having to translate material on C++ or Java, the professional or student VB.NET programmer will find a tutorial on how to use data structures and algorithms and a reference for implementation using VB.NET for data structures and algorithms from the .NET Framework Class Library as well as those which must be developed by the programmer. In an object-oriented fashion, the author presents arrays and arraylists, linked lists, hash tables, dictionaries, trees, graphs, sorting and searching as well as more advanced algorithms, such as probabilistic algorithms and dynamic programming. His approach is very practical, for example using timing tests rather than Big O analysis to compare the performance of data structures and algorithms. This book can be used in both beginning and advanced computer programming courses that use the VB.NET language and, most im

This highly technical work is at the leading edge of spatial analysis. It covers the Global Monitoring for Environment and Security (GMES) initiative in the international context of access to environm

Models and Methods in Social Network Analysis, first published in 2005, presents the most important developments in quantitative models and methods for analyzing social network data that have appeared during the 1990s. Intended as a complement to Wasserman and Faust's Social Network Analysis: Methods and Applications, it is a collection of articles by leading methodologists reviewing advances in their particular areas of network methods. Reviewed are advances in network measurement, network sampling, the analysis of centrality, positional analysis or blockmodelling, the analysis of diffusion through networks, the analysis of affiliation or 'two-mode' networks, the theory of random graphs, dependence graphs, exponential families of random graphs, the analysis of longitudinal network data, graphical techniques for exploring network data, and software for the analysis of social networks.