Information quality data analytics : the potential of data and analytics to generate knowledge

Information quality data analytics the potential of data and analytics to generate knowledge

Detalles Bibliográficos
Otros Autores: Kenett, Ron, author (author), Shmueli, Galit, 1971- author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Chichester, West Sussex, England : Wiley 2017.
Edición:1st ed
Colección:THEi Wiley ebooks.
Materias:
Data mining.
Mathematical statistics.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009849117506719
Tabla de Contenidos:
  • Intro
  • Title Page
  • Copyright Page
  • Contents
  • Foreword
  • About the authors
  • Preface
  • Quotes about the book
  • About the companion website
  • Part I The Information Quality Framework
  • Chapter 1 Introduction to information quality
  • 1.1 Introduction
  • 1.2 Components of InfoQ
  • 1.3 Definition of information quality
  • 1.4 Examples from online auction studies
  • 1.5 InfoQ and study quality
  • 1.6 Summary
  • References
  • Chapter 2 Quality of goal, data quality, and analysis quality
  • 2.1 Introduction
  • 2.2 Data quality
  • 2.3 Analysis quality
  • 2.4 Quality of utility
  • 2.5 Summary
  • References
  • Chapter 3 Dimensions of information quality and InfoQ assessment
  • 3.1 Introduction
  • 3.2 The eight dimensions of InfoQ
  • 3.3 Assessing InfoQ
  • 3.4 Example: InfoQ assessment of online auction experimental data
  • 3.5 Summary
  • References
  • Chapter 4 InfoQ at the study design stage
  • 4.1 Introduction
  • 4.2 Primary versus secondary data and experiments versus observational data
  • 4.3 Statistical design of experiments
  • 4.4 Clinical trials and experiments with human subjects
  • 4.5 Design of observational studies: Survey sampling
  • 4.6 Computer experiments (simulations)
  • 4.7 Multiobjective studies
  • 4.8 Summary
  • References
  • Chapter 5 InfoQ at the postdata collection stage
  • 5.1 Introduction
  • 5.2 Postdata collection data
  • 5.3 Data cleaning and preprocessing
  • 5.4 Reweighting and bias adjustment
  • 5.5 Meta-analysis
  • 5.6 Retrospective experimental design analysis
  • 5.7 Models that account for data "loss": Censoring and truncation
  • 5.8 Summary
  • References
  • Part II Applications of InfoQ
  • Chapter 6 Education
  • 6.1 Introduction
  • 6.2 Test scores in schools
  • 6.3 Value-added models for educational assessment
  • 6.4 Assessing understanding of concepts
  • 6.5 Summary.
  • Appendix: MERLO implementation for an introduction to statistics course
  • References
  • Chapter 7 Customer surveys
  • 7.1 Introduction
  • 7.2 Design of customer surveys
  • 7.3 InfoQ components
  • 7.4 Models for customer survey data analysis
  • 7.5 InfoQ evaluation
  • 7.6 Summary
  • Appendix: A posteriori InfoQ improvement for survey nonresponse selection bias
  • References
  • Chapter 8 Healthcare
  • 8.1 Introduction
  • 8.2 Institute of medicine reports
  • 8.3 Sant'Anna di Pisa report on the Tuscany healthcare system
  • 8.4 The haemodialysis case study
  • 8.5 The Geriatric Medical Center case study
  • 8.6 Report of cancer incidence cluster
  • 8.7 Summary
  • References
  • Chapter 9 Risk management
  • 9.1 Introduction
  • 9.2 Financial engineering, risk management, and Taleb's quadrant
  • 9.3 Risk management of OSS
  • 9.4 Risk management of a telecommunication system supplier
  • 9.5 Risk management in enterprise system implementation
  • 9.6 Summary
  • References
  • Chapter 10 Official statistics
  • 10.1 Introduction
  • 10.2 Information quality and official statistics
  • 10.3 Quality standards for official statistics
  • 10.4 Standards for customer surveys
  • 10.5 Integrating official statistics with administrative data for enhanced InfoQ
  • 10.6 Summary
  • References
  • Part III Implementing InfoQ
  • Chapter 11 InfoQ and reproducible research
  • 11.1 Introduction
  • 11.2 Definitions of reproducibility, repeatability, and replicability
  • 11.3 Reproducibility and repeatability in GR&amp
  • &amp
  • R
  • 11.4 Reproducibility and repeatability in animal behavior studies
  • 11.5 Replicability in genome‐wide association studies
  • 11.6 Reproducibility, repeatability, and replicability: the InfoQ lens
  • 11.7 Summary
  • Appendix: Gauge repeatability and reproducibility study design and analysis
  • References.
  • Chapter 12 InfoQ in review processes of scientific publications
  • 12.1 Introduction
  • 12.2 Current guidelines in applied journals
  • 12.3 InfoQ guidelines for reviewers
  • 12.4 Summary
  • References
  • Chapter 13 Integrating InfoQ into data science analytics programs, research methods courses, and more
  • 13.1 Introduction
  • 13.2 Experience from InfoQ integrations in existing courses
  • 13.3 InfoQ as an integrating theme in analytics programs
  • 13.4 Designing a new analytics course (or redesigning an existing course)
  • 13.5 A one-day InfoQ workshop
  • 13.6 Summary
  • Acknowledgements
  • References
  • Chapter 14 InfoQ support with R
  • 14.1 Introduction
  • 14.2 Examples of information quality with R
  • 14.3 Components and dimensions of InfoQ and R
  • 14.4 Summary
  • References
  • Chapter 15 InfoQ support with Minitab
  • 15.1 Introduction
  • 15.2 Components and dimensions of InfoQ and Minitab
  • 15.3 Examples of InfoQ with Minitab
  • 15.4 Summary
  • References
  • Chapter 16 InfoQ support with JMP
  • 16.1 Introduction
  • 16.2 Example 1: Controlling a film deposition process
  • 16.3 Example 2: Predicting water quality in the Savannah River Basin
  • 16.4 A JMP application to score the InfoQ dimensions
  • 16.5 JMP capabilities and InfoQ
  • 16.6 Summary
  • References
  • Index
  • EULA.

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