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An introduction to data analysis usi...

James, Simon.

An introduction to data analysis using aggregation functions in R

Record Type:	Electronic resources : Monograph/item
Title/Author:	An introduction to data analysis using aggregation functions in Rby Simon James.
Author:	James, Simon.
Published:	Cham :Springer International Publishing :2016.
Description:	x, 199 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Mathematical statisticsData processing.
Online resource:	http://dx.doi.org/10.1007/978-3-319-46762-7
ISBN:	9783319467627$q(electronic bk.)

An introduction to data analysis using aggregation functions in R
James, Simon.

An introduction to data analysis using aggregation functions in R[electronic resource] /by Simon James. - Cham :Springer International Publishing :2016. - x, 199 p. :ill. (some col.), digital ;24 cm.

Aggregating data with averaging functions -- Transforming data -- Weighted averaging -- Averaging with interaction -- Fitting aggregation functions to empirical data -- Solutions.

This textbook helps future data analysts comprehend aggregation function theory and methods in an accessible way, focusing on a fundamental understanding of the data and summarization tools. Offering a broad overview of recent trends in aggregation research, it complements any study in statistical or machine learning techniques. Readers will learn how to program key functions in R without obtaining an extensive programming background. Sections of the textbook cover background information and context, aggregating data with averaging functions, power means, and weighted averages including the Borda count. It explains how to transform data using normalization or scaling and standardization, as well as log, polynomial, and rank transforms. The section on averaging with interaction introduces OWS functions and the Choquet integral, simple functions that allow the handling of non-independent inputs. The final chapters examine software analysis with an emphasis on parameter identification rather than technical aspects. This textbook is designed for students studying computer science or business who are interested in tools for summarizing and interpreting data, without requiring a strong mathematical background. It is also suitable for those working on sophisticated data science techniques who seek a better conception of fundamental data aggregation. Solutions to the practice questions are included in the textbook.

ISBN: 9783319467627$q(electronic bk.)

Standard No.: 10.1007/978-3-319-46762-7doiSubjects--Topical Terms:

183916
Mathematical statistics
--Data processing.

LC Class. No.: QA276.45.R3

Dewey Class. No.: 519.50285

An introduction to data analysis using aggregation functions in R
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505 0 $a Aggregating data with averaging functions -- Transforming data -- Weighted averaging -- Averaging with interaction -- Fitting aggregation functions to empirical data -- Solutions.
520 $a This textbook helps future data analysts comprehend aggregation function theory and methods in an accessible way, focusing on a fundamental understanding of the data and summarization tools. Offering a broad overview of recent trends in aggregation research, it complements any study in statistical or machine learning techniques. Readers will learn how to program key functions in R without obtaining an extensive programming background. Sections of the textbook cover background information and context, aggregating data with averaging functions, power means, and weighted averages including the Borda count. It explains how to transform data using normalization or scaling and standardization, as well as log, polynomial, and rank transforms. The section on averaging with interaction introduces OWS functions and the Choquet integral, simple functions that allow the handling of non-independent inputs. The final chapters examine software analysis with an emphasis on parameter identification rather than technical aspects. This textbook is designed for students studying computer science or business who are interested in tools for summarizing and interpreting data, without requiring a strong mathematical background. It is also suitable for those working on sophisticated data science techniques who seek a better conception of fundamental data aggregation. Solutions to the practice questions are included in the textbook.
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