Applied Multivariate Statistics With R

Author: Daniel Zelterman
Editor: Springer
ISBN: 3319140930
Size: 17,18 MB
Format: PDF, ePub
Read: 317

This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, shareware program R, Professor Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays, linear algebra, univariate, bivariate and multivariate normal distributions, factor methods, linear regression, discrimination and classification, clustering, time series models, and additional methods. Zelterman uses practical examples from diverse disciplines to welcome readers from a variety of academic specialties. Those with backgrounds in statistics will learn new methods while they review more familiar topics. Chapters include exercises, real data sets, and R implementations. The data are interesting, real-world topics, particularly from health and biology-related contexts. As an example of the approach, the text examines a sample from the Behavior Risk Factor Surveillance System, discussing both the shortcomings of the data as well as useful analyses. The text avoids theoretical derivations beyond those needed to fully appreciate the methods. Prior experience with R is not necessary.

R In A Nutshell

Author: Joseph Adler
Editor: O'Reilly Germany
ISBN: 3897216507
Size: 18,54 MB
Format: PDF, ePub, Docs
Read: 685

Wozu sollte man R lernen? Da gibt es viele Gründe: Weil man damit natürlich ganz andere Möglichkeiten hat als mit einer Tabellenkalkulation wie Excel, aber auch mehr Spielraum als mit gängiger Statistiksoftware wie SPSS und SAS. Anders als bei diesen Programmen hat man nämlich direkten Zugriff auf dieselbe, vollwertige Programmiersprache, mit der die fertigen Analyse- und Visualisierungsmethoden realisiert sind – so lassen sich nahtlos eigene Algorithmen integrieren und komplexe Arbeitsabläufe realisieren. Und nicht zuletzt, weil R offen gegenüber beliebigen Datenquellen ist, von der einfachen Textdatei über binäre Fremdformate bis hin zu den ganz großen relationalen Datenbanken. Zudem ist R Open Source und erobert momentan von der universitären Welt aus die professionelle Statistik. R kann viel. Und Sie können viel mit R machen – wenn Sie wissen, wie es geht. Willkommen in der R-Welt: Installieren Sie R und stöbern Sie in Ihrem gut bestückten Werkzeugkasten: Sie haben eine Konsole und eine grafische Benutzeroberfläche, unzählige vordefinierte Analyse- und Visualisierungsoperationen – und Pakete, Pakete, Pakete. Für quasi jeden statistischen Anwendungsbereich können Sie sich aus dem reichen Schatz der R-Community bedienen. Sprechen Sie R! Sie müssen Syntax und Grammatik von R nicht lernen – wie im Auslandsurlaub kommen Sie auch hier gut mit ein paar aufgeschnappten Brocken aus. Aber es lohnt sich: Wenn Sie wissen, was es mit R-Objekten auf sich hat, wie Sie eigene Funktionen schreiben und Ihre eigenen Pakete schnüren, sind Sie bei der Analyse Ihrer Daten noch flexibler und effektiver. Datenanalyse und Statistik in der Praxis: Anhand unzähliger Beispiele aus Medizin, Wirtschaft, Sport und Bioinformatik lernen Sie, wie Sie Daten aufbereiten, mithilfe der Grafikfunktionen des lattice-Pakets darstellen, statistische Tests durchführen und Modelle anpassen. Danach werden Ihnen Ihre Daten nichts mehr verheimlichen.

Amstat News

Size: 13,38 MB
Format: PDF, Docs
Read: 979

Watershed Health Monitoring

Author: Chris Jones
Editor: CRC Press
ISBN: 9781420010145
Size: 14,36 MB
Format: PDF, ePub, Mobi
Read: 818

Watershed Health Monitoring: Emerging Technologies is a concise reference that defines the concept of watershed health and explains that monitoring the health of watersheds is a critical precursor to adaptive resource management on a watershed basis. The focus of the text is a clear description of an innovative "Closed Loop" model that specifies four key aspects of successful monitoring programs: political linkages and support, sound scientific assessment techniques, a community education and awareness component; and a sustainable cost-recovery framework achieved through partnership. Divided into two sections, the book begins with an introduction that defines watershed health, explains how monitoring fits into watershed planning and management, describes frequent shortfalls of monitoring programs around the world (with an emphasis on North America) and proposes a "Closed Loop" approach that will help ensure successful programs. Subsequent chapters detail each component of the "Closed Loop" model with special emphasis on scientific assessment. The second part features a set of six case studies that describe successful real-world applications of the "Closed Loop" approach to watershed monitoring. Written in an easily understood manner with the practitioner in mind, Watershed Health Monitoring balances the need for a detailed yet concise treatment of the topic of watershed health monitoring. It is the first book to recognize the multidisciplinary nature of successful monitoring programs-programs that go far beyond science.

Disease Modelling And Public Health

Editor: Elsevier
ISBN: 0444639691
Size: 16,76 MB
Format: PDF
Read: 424

Disease Modelling and Public Health, Part A, Volume 36 addresses new challenges in existing and emerging diseases with a variety of comprehensive chapters that cover Infectious Disease Modeling, Bayesian Disease Mapping for Public Health, Real time estimation of the case fatality ratio and risk factor of death, Alternative Sampling Designs for Time-To-Event Data with Applications to Biomarker Discovery in Alzheimer's Disease, Dynamic risk prediction for cardiovascular disease: An illustration using the ARIC Study, Theoretical advances in type 2 diabetes, Finite Mixture Models in Biostatistics, and Models of Individual and Collective Behavior for Public Health Epidemiology. As a two part volume, the series covers an extensive range of techniques in the field. It present a vital resource for statisticians who need to access a number of different methods for assessing epidemic spread in population, or in formulating public health policy. Presents a comprehensive, two-part volume written by leading subject experts Provides a unique breadth and depth of content coverage Addresses the most cutting-edge developments in the field Includes chapters on Ebola and the Zika virus; topics which have grown in prominence and scholarly output

Statistical Topics In Health Economics And Outcomes Research

Author: Demissie Alemayehu, PhD
Editor: CRC Press
ISBN: 1498781888
Size: 16,94 MB
Format: PDF, ePub, Docs
Read: 658

With ever-rising healthcare costs, evidence generation through Health Economics and Outcomes Research (HEOR) plays an increasingly important role in decision-making about the allocation of resources. Accordingly, it is now customary for health technology assessment and reimbursement agencies to request for HEOR evidence, in addition to data from clinical trials, to inform decisions about patient access to new treatment options. While there is a great deal of literature on HEOR, there is a need for a volume that presents a coherent and unified review of the major issues that arise in application, especially from a statistical perspective. Statistical Topics in Health Economics and Outcomes Research fulfils that need by presenting an overview of the key analytical issues and best practice. Special attention is paid to key assumptions and other salient features of statistical methods customarily used in the area, and appropriate and relatively comprehensive references are made to emerging trends. The content of the book is purposefully designed to be accessible to readers with basic quantitative backgrounds, while providing an in-depth coverage of relatively complex statistical issues. The book will make a very useful reference for researchers in the pharmaceutical industry, academia, and research institutions involved with HEOR studies. The targeted readers may include statisticians, data scientists, epidemiologists, outcomes researchers, health economists, and healthcare policy and decision-makers.

Applied Demography And Public Health In The 21st Century

Author: M. Nazrul Hoque
Editor: Springer
ISBN: 3319436880
Size: 12,40 MB
Format: PDF, ePub
Read: 869

This book demonstrates different statistical techniques for analyzing health-related data as well as providing new techniques for forecasting and/or projecting the incidence of diseases/disorders. It presents information on a variety of health related issues from the developed and developing world. Featuring cutting edge research from distinguished applied demographers and public health specialists, the book bridges the gap between theory and research. Each chapter provides methods and materials that can be used to conduct further research aimed at promoting public health issues. This book is intended for public health professionals, health policy makers, social epidemiologists, administrators, researchers, and students in the fields of applied demography and public health who are interested in exploring the potential of ground-breaking research or who want to further develop their existing research techniques. It complements another volume in the Applied Demography Series, Applied Demography and Public Health (Springer, 2013), which describes how applied demographic techniques can be used to help address public health issues.

The Analysis Of Covariance And Alternatives

Author: Bradley Huitema
Editor: John Wiley & Sons
ISBN: 9781118067468
Size: 20,12 MB
Format: PDF, ePub, Mobi
Read: 379

A complete guide to cutting-edge techniques and best practices for applying covariance analysis methods The Second Edition of Analysis of Covariance and Alternatives sheds new light on its topic, offering in-depth discussions of underlying assumptions, comprehensive interpretations of results, and comparisons of distinct approaches. The book has been extensively revised and updated to feature an in-depth review of prerequisites and the latest developments in the field. The author begins with a discussion of essential topics relating to experimental design and analysis, including analysis of variance, multiple regression, effect size measures and newly developed methods of communicating statistical results. Subsequent chapters feature newly added methods for the analysis of experiments with ordered treatments, including two parametric and nonparametric monotone analyses as well as approaches based on the robust general linear model and reversed ordinal logistic regression. Four groundbreaking chapters on single-case designs introduce powerful new analyses for simple and complex single-case experiments. This Second Edition also features coverage of advanced methods including: Simple and multiple analysis of covariance using both the Fisher approach and the general linear model approach Methods to manage assumption departures, including heterogeneous slopes, nonlinear functions, dichotomous dependent variables, and covariates affected by treatments Power analysis and the application of covariance analysis to randomized-block designs, two-factor designs, pre- and post-test designs, and multiple dependent variable designs Measurement error correction and propensity score methods developed for quasi-experiments, observational studies, and uncontrolled clinical trials Thoroughly updated to reflect the growing nature of the field, Analysis of Covariance and Alternatives is a suitable book for behavioral and medical scineces courses on design of experiments and regression and the upper-undergraduate and graduate levels. It also serves as an authoritative reference work for researchers and academics in the fields of medicine, clinical trials, epidemiology, public health, sociology, and engineering.

Statistik Workshop F R Programmierer

Author: Allen B. Downey
Editor: O'Reilly Germany
ISBN: 3868993436
Size: 17,98 MB
Format: PDF, ePub, Docs
Read: 948

Wenn Sie programmieren können, beherrschen Sie bereits Techniken, um aus Daten Wissen zu extrahieren. Diese kompakte Einführung in die Statistik zeigt Ihnen, wie Sie rechnergestützt, anstatt auf mathematischem Weg Datenanalysen mit Python durchführen können. Praktischer Programmier-Workshop statt grauer Theorie: Das Buch führt Sie anhand eines durchgängigen Fallbeispiels durch eine vollständige Datenanalyse -- von der Datensammlung über die Berechnung statistischer Kennwerte und Identifikation von Mustern bis hin zum Testen statistischer Hypothesen. Gleichzeitig werden Sie mit statistischen Verteilungen, den Regeln der Wahrscheinlichkeitsrechnung, Visualisierungsmöglichkeiten und vielen anderen Arbeitstechniken und Konzepten vertraut gemacht. Statistik-Konzepte zum Ausprobieren: Entwickeln Sie über das Schreiben und Testen von Code ein Verständnis für die Grundlagen von Wahrscheinlichkeitsrechnung und Statistik: Überprüfen Sie das Verhalten statistischer Merkmale durch Zufallsexperimente, zum Beispiel indem Sie Stichproben aus unterschiedlichen Verteilungen ziehen. Nutzen Sie Simulationen, um Konzepte zu verstehen, die auf mathematischem Weg nur schwer zugänglich sind. Lernen Sie etwas über Themen, die in Einführungen üblicherweise nicht vermittelt werden, beispielsweise über die Bayessche Schätzung. Nutzen Sie Python zur Bereinigung und Aufbereitung von Rohdaten aus nahezu beliebigen Quellen. Beantworten Sie mit den Mitteln der Inferenzstatistik Fragestellungen zu realen Daten.