00000cam a2200000 i 4500 UP-99796217613765226 Buklod 20230202101108.0 a r |||| u| ta 230202s2018 njuad e 001 0 eng d 9781118897157 (hbd.) (iLib)UPD-00524273906 Pu-29761 PhP5,462.50 DLC DCPA rda eng QA 276.45 R3 V36 2018 Van der Loo, Mark author Statistical data cleaning with applications in R Mark van der Loo and Edwin de Jonge. Hoboken, New Jersey John Wiley & Sons 2018. xiii, 300 pages 25 cm text rdacontent unmediated rdamedia volume rdacarrier Includes bibliographical references and index. 1. Data cleaning. -- 2. A brief introduction to R. -- 3. Technical representation of data. -- 4. Data structure. -- 5. Cleaning text data. -- 6. Data validation. -- 7. Localizing errors in data records. --8. Rule set maintenance and simplification. -- 9. Methods on models for domain knowledge. -- 10. Imputation and adjustment. -- 11. Example: a small data-cleaning system. -- References. -- Index. A comprehensive guide to automated statistical data cleaning The production of clean data is a complex and time-consuming process that requires both technical know-how and statistical expertise. Statistical Data Cleaning brings together a wide range of techniques for cleaning textual, numeric or categorical data. This book examines technical data cleaning methods relating to data representation and data structure. A prominent role is given to statistical data validation, data cleaning based on predefined restrictions, and data cleaning strategy. Key features: Focuses on the automation of data cleaning methods, including both theory and applications written in R. Enables the reader to design data cleaning processes for either one-off analytical purposes or for setting up production systems that clean data on a regular basis. Explores statistical techniques for solving issues such as incompleteness, contradictions and outliers, integration of data cleaning components and quality monitoring. Supported by an accompanying website featuring data and R code. This book enables data scientists and statistical analysts working with data to deepen their understanding of data cleaning as well as to upgrade their practical data cleaning skills. It can also be used as material for a course in data cleaning and analyses. R (Computer program language) Statistics Data processing. Research Methods in Public Administration I. PA 199.1. Research Methods in Public Administration II. PA 199.2. Research Methods in Public Administration I. PA 299.1. Research Methods in Public Administration II. PA 299.2. De Jonge, Edwin author. Book FO UPD DCPA QA 276.45 R3 V36 2018 Book