Statistical Data Analysis Explained

Applied Environmental Statistics with R

Author: Clemens Reimann,Peter Filzmoser,Robert Garrett,Rudolf Dutter

Publisher: John Wiley & Sons

ISBN: 1119965284

Category: Science

Page: 362

View: 8754

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Few books on statistical data analysis in the natural sciences are written at a level that a non-statistician will easily understand. This is a book written in colloquial language, avoiding mathematical formulae as much as possible, trying to explain statistical methods using examples and graphics instead. To use the book efficiently, readers should have some computer experience. The book starts with the simplest of statistical concepts and carries readers forward to a deeper and more extensive understanding of the use of statistics in environmental sciences. The book concerns the application of statistical and other computer methods to the management, analysis and display of spatial data. These data are characterised by including locations (geographic coordinates), which leads to the necessity of using maps to display the data and the results of the statistical methods. Although the book uses examples from applied geochemistry, and a large geochemical survey in particular, the principles and ideas equally well apply to other natural sciences, e.g., environmental sciences, pedology, hydrology, geography, forestry, ecology, and health sciences/epidemiology. The book is unique because it supplies direct access to software solutions (based on R, the Open Source version of the S-language for statistics) for applied environmental statistics. For all graphics and tables presented in the book, the R-scripts are provided in the form of executable R-scripts. In addition, a graphical user interface for R, called DAS+R, was developed for convenient, fast and interactive data analysis. Statistical Data Analysis Explained: Applied Environmental Statistics with R provides, on an accompanying website, the software to undertake all the procedures discussed, and the data employed for their description in the book.

Geographical Data Analysis

Author: Nigel Walford

Publisher: John Wiley & Sons Incorporated

ISBN: N.A

Category: Business & Economics

Page: 446

View: 7821

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It is increasingly important for the earth science student to appreciate that the acquisition of skills in statistics and computerised data analysis is as much part of modern geography as work in the field, laboratory or library. In this respect, Geographical Data Analysis aims to link the use of statistical techniques by means of computer software, to the acquisition of geographical-knowledge and the scientific method of enquiry. The book has three objectives: to explain basic statistical techniques and demonstrate their application to quantitative geography; to equip students with the knowledge and skills necessary for carrying out research projects; and to make the link between statistical analysis and the substantive topics taught as part of a geography course. An important innovative feature of the book is its project-orientated approach, which utilises exemplar projects drawn from human and physical geography. Each exemplar project shows the progress from the conception of the initial research through to the formulation of tentative hypotheses and the subsequent statistical analysis. The projects exemplify both primary and secondary methods for collecting geographical data, with the computer-based application of a wide range of statistical techniques. Thus, these projects allow discussion of sample design, data collection and computerisation, and a selection of appropriate statistical techniques. As such, Geographical Data Analysis integrates quantitative and geographical methodologies and provides a thorough understanding of basic statistical techniques for the undergraduate geography student; it will be of use from first year through to final degree dissertations.

Advanced Quantitative Data Analysis

Author: Cramer, Duncan

Publisher: McGraw-Hill Education (UK)

ISBN: 0335200591

Category: Social Science

Page: 254

View: 3087

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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. The following features characterize 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.

Introduction to Statistics and Data Analysis

Author: Roxy Peck,Chris Olsen,Jay L. Devore

Publisher: Cengage Learning

ISBN: 0840054904

Category: Mathematics

Page: 944

View: 5084

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Roxy Peck, Chris Olsen, and Jay Devore's new edition uses real data and attention-grabbing examples to introduce students to the study of statistics and data analysis. Traditional in structure yet modern in approach, this text guides students through an intuition-based learning process that stresses interpretation and communication of statistical information. Simple notation--including the frequent substitution of words for symbols--helps students grasp concepts and cement their comprehension. Hands-on activities and interactive applets allow students to practice statistics firsthand. INTRODUCTION TO STATISTICS AND DATA ANALYSIS, 4th Edition, includes updated coverage of the graphing calculator as well as expanded coverage of probability. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Statistical Methods for Practice and Research

A Guide to Data Analysis Using SPSS

Author: Ajai S Gaur,Sanjaya S Gaur

Publisher: SAGE Publishing India

ISBN: 8132118596

Category: Business & Economics

Page: 172

View: 9105

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There is a growing trend these days to use statistical methods to comprehend and explain various situations and phenomena in different disciplines. Managers, social scientists and practicing researchers are increasingly collecting information and applying scientific methods to analyze the data. The ability to use statistical methods and tools becomes a crucial skill for the success of such efforts. This book is designed to assist students, managers, academics and researchers in solving statistical problems using SPSS and to help them understand how they can apply various statistical tools for their own research problems. SPSS is a very powerful and user friendly computer package for data analyses. It can take data from most other file types and generate tables, charts, plots, and descriptive statistics, and conduct complex statistical analyses. After providing a brief overview of SPSS and basic statistical concepts, the book covers: - Descriptive statistics - t-tests, chi-square tests and ANOVA - Correlation analysis - Multiple and logistics regression - Factor analysis and testing scale reliability - Advanced data handling Illustrated with simple, practical problems, and screen shots, this book outlines the steps for solving statistical problems using SPSS. Although the illustrations are based on version 16.0 of SPSS, users of the earlier versions will find the book equally useful and relevant. Written in a reader-friendly, non-technical style, this book will serve as a companion volume to any statistics textbook.

Data Analysis and Presentation Skills

An Introduction for the Life and Medical Sciences

Author: Jackie Willis

Publisher: John Wiley & Sons

ISBN: 0470011319

Category: Science

Page: 196

View: 9724

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Data Analysis and Presentation Skills: An Introduction for the Life and Medical Sciences is an invaluable text allowing students to develop appropriate key skills when designing experiments, generating results, analysing data and ultimately presenting findings to academics and referees. Taking a hands-on approach, each of these key areas is introduced clearly and carefully, showing how to access and evaluate information using a variety of resources. Basic analytical theory is gradually introduced alongside practical applications to enhance student understanding. The reader is shown how to present data in charts using Microsoft Excel and statistical analysis is carefully explained showing clearly how to manipulate data in spreadsheets and analyse the results using commonly used tests. A section is also included on the use of PowerPoint as well as giving advice on how to prepare a short talk or seminar. Includes numerous relevant examples and case studies drawn from the Life Sciences Guidance on how to complete and present practical and project work through to postgraduate dissertation. Clear step-by-step introduction to Microsoft Excel, presentation skills and statistical analysis. Invaluable for all students within the Life and Medical Sciences

Statistik-Workshop für Programmierer

Author: Allen B. Downey

Publisher: O'Reilly Germany

ISBN: 3868993436

Category: Computers

Page: 160

View: 6454

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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.

Einführung in Statistik und Messwertanalyse für Physiker

Monographie

Author: G. Bohm,G. Zech

Publisher: N.A

ISBN: 9783540257592

Category:

Page: 400

View: 7773

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Die Einf]hrung in die Statistik und Messwertanalyse f]r Physiker richtet sich weniger an mathematischen \berlegungen aus, sondern stellt die praktische Anwendung in den Vordergrund und schdrft die Intuition experimentelle Ergebnisse richtig einzuschdtzen. Zahlreiche ausf]hrlich betrachtete Beispiele dienen dazu, hdufig bei der Datenanalyse gemachte Fehler zu vermeiden (unsinnige Anwendung des Chi-Quadrattests, Funktionenanpassung bei falscher Parametrisierung, Entfaltung mit willk]rlicher Regularisierung). Ein besonderes Augenmerk wird auf den Vergleich von Daten mit Monte-Carlo-Simulationen gelenkt. Moderne Experimente kommen nicht ohne Simulation aus. Deshalb ist es wichtig zu wissen, wie Parameteranpassungen und Entfaltungen in diesem Fall durchgef]rt werden. Au_erdem werden den Studierenden moderne Entwicklungen der Statistik nahegebracht, die in dlteren Lehrb]chern nicht behandelt werden.

Adverse Impact Analysis

Understanding Data, Statistics, and Risk

Author: Scott B. Morris,Eric M. Dunleavy

Publisher: Psychology Press

ISBN: 1315301423

Category: Business & Economics

Page: 400

View: 8995

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Compliance with federal equal employment opportunity regulations, including civil rights laws and affirmative action requirements, requires collection and analysis of data on disparities in employment outcomes, often referred to as adverse impact. While most human resources (HR) practitioners are familiar with basic adverse impact analysis, the courts and regulatory agencies are increasingly relying on more sophisticated methods to assess disparities. Employment data are often complicated, and can include a broad array of employment actions (e.g., selection, pay, promotion, termination), as well as data that span multiple protected groups, settings, and points in time. In the era of "big data," the HR analyst often has access to larger and more complex data sets relevant to employment disparities. Consequently, an informed HR practitioner needs a richer understanding of the issues and methods for conducting disparity analyses. This book brings together the diverse literature on disparity analysis, spanning work from statistics, industrial/organizational psychology, human resource management, labor economics, and law, to provide a comprehensive and integrated summary of current best practices in the field. Throughout, the description of methods is grounded in the legal context and current trends in employment litigation and the practices of federal regulatory agencies. The book provides guidance on all phases of disparity analysis, including: How to structure diverse and complex employment data for disparity analysis How to conduct both basic and advanced statistical analyses on employment outcomes related to employee selection, promotion, compensation, termination, and other employment outcomes How to interpret results in terms of both practical and statistical significance Common practical challenges and pitfalls in disparity analysis and strategies to deal with these issues

Statistical Methods for Astronomical Data Analysis

Author: Asis Kumar Chattopadhyay,Tanuka Chattopadhyay

Publisher: Springer

ISBN: 149391507X

Category: Mathematics

Page: 349

View: 1099

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This book introduces “Astrostatistics” as a subject in its own right with rewarding examples, including work by the authors with galaxy and Gamma Ray Burst data to engage the reader. This includes a comprehensive blending of Astrophysics and Statistics. The first chapter’s coverage of preliminary concepts and terminologies for astronomical phenomenon will appeal to both Statistics and Astrophysics readers as helpful context. Statistics concepts covered in the book provide a methodological framework. A unique feature is the inclusion of different possible sources of astronomical data, as well as software packages for converting the raw data into appropriate forms for data analysis. Readers can then use the appropriate statistical packages for their particular data analysis needs. The ideas of statistical inference discussed in the book help readers determine how to apply statistical tests. The authors cover different applications of statistical techniques already developed or specifically introduced for astronomical problems, including regression techniques, along with their usefulness for data set problems related to size and dimension. Analysis of missing data is an important part of the book because of its significance for work with astronomical data. Both existing and new techniques related to dimension reduction and clustering are illustrated through examples. There is detailed coverage of applications useful for classification, discrimination, data mining and time series analysis. Later chapters explain simulation techniques useful for the development of physical models where it is difficult or impossible to collect data. Finally, coverage of the many R programs for techniques discussed makes this book a fantastic practical reference. Readers may apply what they learn directly to their data sets in addition to the data sets included by the authors.

SPSS Statistics for Data Analysis and Visualization

Author: Keith McCormick,Jesus Salcedo,Jon Peck,Andrew Wheeler

Publisher: John Wiley & Sons

ISBN: 1119003555

Category: Computers

Page: 528

View: 7110

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Dive deeper into SPSS Statistics for more efficient, accurate, and sophisticated data analysis and visualization SPSS Statistics for Data Analysis and Visualization goes beyond the basics of SPSS Statistics to show you advanced techniques that exploit the full capabilities of SPSS. The authors explain when and why to use each technique, and then walk you through the execution with a pragmatic, nuts and bolts example. Coverage includes extensive, in-depth discussion of advanced statistical techniques, data visualization, predictive analytics, and SPSS programming, including automation and integration with other languages like R and Python. You'll learn the best methods to power through an analysis, with more efficient, elegant, and accurate code. IBM SPSS Statistics is complex: true mastery requires a deep understanding of statistical theory, the user interface, and programming. Most users don't encounter all of the methods SPSS offers, leaving many little-known modules undiscovered. This book walks you through tools you may have never noticed, and shows you how they can be used to streamline your workflow and enable you to produce more accurate results. Conduct a more efficient and accurate analysis Display complex relationships and create better visualizations Model complex interactions and master predictive analytics Integrate R and Python with SPSS Statistics for more efficient, more powerful code These "hidden tools" can help you produce charts that simply wouldn't be possible any other way, and the support for other programming languages gives you better options for solving complex problems. If you're ready to take advantage of everything this powerful software package has to offer, SPSS Statistics for Data Analysis and Visualization is the expert-led training you need.

Handbook of Statistical Analysis and Data Mining Applications

Author: Robert Nisbet,John Elder,Gary Miner

Publisher: Academic Press

ISBN: 9780080912035

Category: Mathematics

Page: 864

View: 8076

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The Handbook of Statistical Analysis and Data Mining Applications 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 one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book 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 industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions. Written "By Practitioners for Practitioners" Non-technical explanations build understanding without jargon and equations Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models Practical advice from successful real-world implementations Includes extensive case studies, examples, MS PowerPoint slides and datasets CD-DVD with valuable fully-working 90-day software included: "Complete Data Miner - QC-Miner - Text Miner" bound with book

Computer-aided multivariate analysis

Author: Abdelmonem A. Afifi,Virginia Clark

Publisher: N.A

ISBN: N.A

Category: Mathematics

Page: 458

View: 2444

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Increasingly, researchers need to perform multivariate statistical analyses on their data. Unfortunately, a lack of mathematical training prevents many from taking advantage of these advanced techniques, in part, because books focus on the theory & neglect to explain how to perform & interpret multivariate analyses on real-life data. For years, Afifi & Clark's Computer-Aided Multivariate Analysis has been a welcome exception-helping researchers choose the appropriate analyses for their data, carry them out, & interpret the results. Only a limited knowledge of statistics is assumed, & geometrical & graphical explanations are used to explain what the analyses do. However, the basic model is always given, & assumptions are discussed. Reflecting the increased emphasis on computers, the Third Edition includes three additional statistical packages written for the personal computer. The authors also discuss data entry, database management, data screening, data transformations, as well as multivariate data analysis. Another new chapter focuses on log-linear analysis of multi-way frequency tables. Students in a wide range of fields-ranging from psychology, sociology, & physical sciences to public health & biomedical science-will find Computer-Aided Multivariate Analysis especially informative & enlightening.

A Casebook for Spatial Statistical Data Analysis

A Compilation of Analyses of Different Thematic Data Sets

Author: Daniel A. Griffith,Larry J. Layne,J. K. Ord,Akio Sone

Publisher: Oxford University Press on Demand

ISBN: 0195109589

Category: Mathematics

Page: 506

View: 2498

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This volume compiles geostatistical and spatial autoregressive data analyses involving georeferenced socioeconomic, natural resources, agricultural, pollution, and epidemiological variables. Benchmark analyses are followed by analyses of readily available data sets, emphasizing parallels between geostatistical and spatial autoregressive findings. Both SAS and SPSS code are presented for implementation purposes. This informative casebook will serve geographers, regional scientists, applied spatial statisticians, and spatial scientists from across disciplines.

Data Analysis and Data Mining

An Introduction

Author: Adelchi Azzalini,Bruno Scarpa

Publisher: Oxford University Press

ISBN: 0199942714

Category: Business & Economics

Page: 288

View: 3122

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An introduction to statistical data mining, Data Analysis and Data Mining is both textbook and professional resource. Assuming only a basic knowledge of statistical reasoning, it presents core concepts in data mining and exploratory statistical models to students and professional statisticians-both those working in communications and those working in a technological or scientific capacity-who have a limited knowledge of data mining. This book presents key statistical concepts by way of case studies, giving readers the benefit of learning from real problems and real data. Aided by a diverse range of statistical methods and techniques, readers will move from simple problems to complex problems. Through these case studies, authors Adelchi Azzalini and Bruno Scarpa explain exactly how statistical methods work; rather than relying on the "push the button" philosophy, they demonstrate how to use statistical tools to find the best solution to any given problem. Case studies feature current topics highly relevant to data mining, such web page traffic; the segmentation of customers; selection of customers for direct mail commercial campaigns; fraud detection; and measurements of customer satisfaction. Appropriate for both advanced undergraduate and graduate students, this much-needed book will fill a gap between higher level books, which emphasize technical explanations, and lower level books, which assume no prior knowledge and do not explain the methodology behind the statistical operations.

Introduction to Statistical Data Analysis for the Life Sciences, Second Edition

Author: Claus Thorn Ekstrom,Helle Sørensen

Publisher: CRC Press

ISBN: 1482238942

Category: Mathematics

Page: 526

View: 1348

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A Hands-On Approach to Teaching Introductory Statistics Expanded with over 100 more pages, Introduction to Statistical Data Analysis for the Life Sciences, Second Edition presents the right balance of data examples, statistical theory, and computing to teach introductory statistics to students in the life sciences. This popular textbook covers the mathematics underlying classical statistical analysis, the modeling aspects of statistical analysis and the biological interpretation of results, and the application of statistical software in analyzing real-world problems and datasets. New to the Second Edition A new chapter on non-linear regression models A new chapter that contains examples of complete data analyses, illustrating how a full-fledged statistical analysis is undertaken Additional exercises in most chapters A summary of statistical formulas related to the specific designs used to teach the statistical concepts This text provides a computational toolbox that enables students to analyze real datasets and gain the confidence and skills to undertake more sophisticated analyses. Although accessible with any statistical software, the text encourages a reliance on R. For those new to R, an introduction to the software is available in an appendix. The book also includes end-of-chapter exercises as well as an entire chapter of case exercises that help students apply their knowledge to larger datasets and learn more about approaches specific to the life sciences.

Statistical Analysis of FMRI Data

Author: F. Gregory Ashby

Publisher: MIT Press

ISBN: 0262015048

Category: Medical

Page: 332

View: 3779

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An overview of statistical methods for analyzing data from fMRI experiments. Functional magnetic resonance imaging (fMRI), which allows researchers to observe neural activity in the human brain noninvasively, has revolutionized the scientific study of the mind. An fMRI experiment produces massive amounts of highly complex data; researchers face significant challenges in analyzing the data they collect. This book offers an overview of the most widely used statistical methods of analyzing fMRI data. Every step is covered, from preprocessing to advanced methods for assessing functional connectivity. The goal is not to describe which buttons to push in the popular software packages but to help readers understand the basic underlying logic, the assumptions, the strengths and weaknesses, and the appropriateness of each method. The book covers all of the important current topics in fMRI data analysis, including the relation of the fMRI BOLD (blood oxygen-level dependent) response to neural activation; basic analyses done in virtually every fMRI article--preprocessing, constructing statistical parametrical maps using the general linear model, solving the multiple comparison problem, and group analyses; the most popular methods for assessing functional connectivity--coherence analysis and Granger causality; two widely used multivariate approaches, principal components analysis and independent component analysis; and a brief survey of other current fMRI methods. The necessary mathematics is explained at a conceptual level, but in enough detail to allow mathematically sophisticated readers to gain more than a purely conceptual understanding. The book also includes short examples of Matlab code that implement many of the methods described; an appendix offers an introduction to basic Matlab matrix algebra commands (as well as a tutorial on matrix algebra). A second appendix introduces multivariate probability distributions.

Your statistical consultant

answers to your data analysis questions

Author: Rae R. Newton,Kjell Erik Rudestam

Publisher: Sage Publications, Inc

ISBN: 9780803958227

Category: Mathematics

Page: 313

View: 7032

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This guide aims to introduce, describe and make recommendations regarding difficult statistical problems and techniques. It seeks to address common problems, provide a conceptual view of topics, and highlight and explain the key terms.