Automated Data Collection with R

Panel Data Econometrics with R provides a tutorial for using R in the field of panel data econometrics. Illustrated throughout with examples in econometrics, political science, agriculture and epidemiology, this book presents classic methodology and applications as well as more advanced topics and recent developments in this field including error component models, spatial panels and dynamic models. They have developed the software programming in R and host replicable material on the book’s accompanying website.

Data Collection Data Collection is the second of six books in the Measurement and Evaluation Series from Pfeiffer. The proven ROI Methodology–developed by the ROI Institute–provides a practical system for evaluation planning, data collection, data analysis, and reporting. All six books in the series offer the latest tools, most current research, and practical advice for measuring ROI in a variety of settings. Data Collection offers an effective process for collecting data that is essential to the implementation of the ROI Methodology. The authors outline the techniques, processes, and critical issues involved in successful data collection. The book examines the various methods of data collection, including questionnaires, interviews, focus groups, observation, action plans, performance contracts, and monitoring records. Written for evaluators, facilitators, analysts, designers, coordinators, and managers, Data Collection is a valuable guide for collecting data that are adequate in quantity and quality to produce a complete and credible analysis.

A comprehensive discussion of automated transit This book analyzes the successful implementations of automated transit in various international locations, such as Paris, Toronto, London, and Kuala Lumpur, and investigates the apparent lack of automated transit applications in the urban environment in the United States. The book begins with a brief definition of automated transit and its historical development. After a thorough description of the technical specifications, the author highlights a few applications from each sub-group of the automated transit spectrum. International case studies display various technologies and their applications, and identify vital factors that affect each system and performance evaluations of existing applications. The book then discusses the planning and operation of automated transit applications at both macro and micro levels. Finally, the book covers a number of less successful concepts, as well as the lessons learned, allowing readers to gain a comprehensive understanding of the topic. Key features: Provides a thorough examination of automated transit applications, their impact and implications for society Written by the committee chair for the Automated Transit Systems Transportation, Research Board Offers essential information on planning, costs, and applications of automated transit systems Covers driverless metros, automated LRT, group and personal rapid transit, a review of worldwide applications Includes capacity and safety guidelines, as well as vehicles, propulsion, and communication and control systems This book is essential reading for engineers, researchers, scientists, college or graduate students who work in transportation planning, engineering, operation and management fields.

Create a competitive advantage with data quality Data is rapidly becoming the powerhouse of industry, but low-quality data can actually put a company at a disadvantage. To be used effectively, data must accurately reflect the real-world scenario it represents, and it must be in a form that is usable and accessible. Quality data involves asking the right questions, targeting the correct parameters, and having an effective internal management, organization, and access system. It must be relevant, complete, and correct, while falling in line with pervasive regulatory oversight programs. Competing with High Quality Data: Concepts, Tools and Techniques for Building a Successful Approach to Data Quality takes a holistic approach to improving data quality, from collection to usage. Author Rajesh Jugulum is globally-recognized as a major voice in the data quality arena, with high-level backgrounds in international corporate finance. In the book, Jugulum provides a roadmap to data quality innovation, covering topics such as: The four-phase approach to data quality control Methodology that produces data sets for different aspects of a business Streamlined data quality assessment and issue resolution A structured, systematic, disciplined approach to effective data gathering The book also contains real-world case studies to illustrate how companies across a broad range of sectors have employed data quality systems, whether or not they succeeded, and what lessons were learned. High-quality data increases value throughout the information supply chain, and the benefits extend to the client, employee, and shareholder. Competing with High Quality Data: Concepts, Tools and Techniques for Building a Successful Approach to Data Quality provides the information and guidance necessary to formulate and activate an effective data quality plan today.

This book provides practical guidance for statisticians, clinicians, and researchers involved in clinical trials in the biopharmaceutical industry, medical and public health organisations. Academics and students needing an introduction to handling missing data will also find this book invaluable. The authors describe how missing data can affect the outcome and credibility of a clinical trial, show by examples how a clinical team can work to prevent missing data, and present the reader with approaches to address missing data effectively. The book is illustrated throughout with realistic case studies and worked examples, and presents clear and concise guidelines to enable good planning for missing data. The authors show how to handle missing data in a way that is transparent and easy to understand for clinicians, regulators and patients. New developments are presented to improve the choice and implementation of primary and sensitivity analyses for missing data. Many SAS code examples are included – the reader is given a toolbox for implementing analyses under a variety of assumptions.

This book addresses the difficulties experienced by wet lab researchers with the statistical analysis of molecular biology related data. The authors explain how to use R and Bioconductor for the analysis of experimental data in the field of molecular biology. The content is based upon two university courses for bioinformatics and experimental biology students (Biological Data Analysis with R and High-throughput Data Analysis with R). The material is divided into chapters based upon the experimental methods used in the laboratories. Key features include: • Broad appeal–the authors target their material to researchers in several levels, ensuring that the basics are always covered. • First book to explain how to use R and Bioconductor for the analysis of several types of experimental data in the field of molecular biology. • Focuses on R and Bioconductor, which are widely used for data analysis. One great benefit of R and Bioconductor is that there is a vast user community and very active discussion in place, in addition to the practice of sharing codes. Further, R is the platform for implementing new analysis approaches, therefore novel methods are available early for R users.