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Advanced modeling and data challenges

Nguyen, Mike.

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Advanced modeling and data challenges

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advanced modeling and data challenges/ by Mike Nguyen.
Author:	Nguyen, Mike.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xiii, 449 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Quantitative research. -
Online resource:	https://doi.org/10.1007/978-3-032-01719-2
ISBN:	9783032017192

Advanced modeling and data challenges
Nguyen, Mike.

Advanced modeling and data challenges[electronic resource] /by Mike Nguyen. - Cham :Springer Nature Switzerland :2025. - xiii, 449 p. :ill., digital ;24 cm.

This volume addresses the complexities of real-world data analysis, offering clear guidance through common challenges such as missing data, model specification, variable selection, and hypothesis testing. It also introduces tools for deeper interpretation-including marginal effects, interaction (moderation), and mediation analysis-to help uncover nuanced relationships in data. Ideal for both learners deepening their understanding and practitioners refining their techniques, this volume blends statistical theory with applied insight. Readers are equipped to navigate ambiguity, confront data imperfections, and extract actionable meaning. With practical examples and an emphasis on analytical reasoning, this book supports coursework, exam review, and professional development alike-making it an essential resource for building confidence in modern data analysis.

ISBN: 9783032017192

Standard No.: 10.1007/978-3-032-01719-2doiSubjects--Topical Terms:

919734
Quantitative research.

LC Class. No.: QA76.9.Q36

Dewey Class. No.: 001.42

Advanced modeling and data challenges
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245 1 0 $a Advanced modeling and data challenges $h [electronic resource] / $c by Mike Nguyen.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
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520 $a This volume addresses the complexities of real-world data analysis, offering clear guidance through common challenges such as missing data, model specification, variable selection, and hypothesis testing. It also introduces tools for deeper interpretation-including marginal effects, interaction (moderation), and mediation analysis-to help uncover nuanced relationships in data. Ideal for both learners deepening their understanding and practitioners refining their techniques, this volume blends statistical theory with applied insight. Readers are equipped to navigate ambiguity, confront data imperfections, and extract actionable meaning. With practical examples and an emphasis on analytical reasoning, this book supports coursework, exam review, and professional development alike-making it an essential resource for building confidence in modern data analysis.
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