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Applied linear regression for busine...

McGibney, Daniel P.

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Applied linear regression for business analytics with R = a practical guide to data science with case studies /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applied linear regression for business analytics with R/ by Daniel P. McGibney.
Reminder of title:	a practical guide to data science with case studies /
Author:	McGibney, Daniel P.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	xvii, 276 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Introduction -- 2. Basic Statistics and Functions using R -- 3. Regression Fundamentals -- 4. Simple Linear Regression -- 5. Multiple Regression -- 6. Estimation Intervals and Analysis of Variance -- 7. Predictor Variable Transformations -- 8. Model Diagnostics -- 9. Variable Selection.
Contained By:	Springer Nature eBook
Subject:	Business - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-031-21480-6
ISBN:	9783031214806

Applied linear regression for business analytics with R = a practical guide to data science with case studies /
McGibney, Daniel P.

Applied linear regression for business analytics with Ra practical guide to data science with case studies /[electronic resource] :by Daniel P. McGibney. - Cham :Springer International Publishing :2023. - xvii, 276 p. :ill., digital ;24 cm. - International series in operations research & management science,v. 3372214-7934 ;. - International series in operations research & management science ;v. 337..

1. Introduction -- 2. Basic Statistics and Functions using R -- 3. Regression Fundamentals -- 4. Simple Linear Regression -- 5. Multiple Regression -- 6. Estimation Intervals and Analysis of Variance -- 7. Predictor Variable Transformations -- 8. Model Diagnostics -- 9. Variable Selection.

Applied Linear Regression for Business Analytics with R introduces regression analysis to business students using the R programming language with a focus on illustrating and solving real-time, topical problems. Specifically, this book presents modern and relevant case studies from the business world, along with clear and concise explanations of the theory, intuition, hands-on examples, and the coding required to employ regression modeling. Each chapter includes the mathematical formulation and details of regression analysis and provides in-depth practical analysis using the R programming language.

ISBN: 9783031214806

Standard No.: 10.1007/978-3-031-21480-6doiSubjects--Topical Terms:

527441
Business
--Data processing.

LC Class. No.: HD30.2 / .M34 2023

Dewey Class. No.: 650.0285

Applied linear regression for business analytics with R = a practical guide to data science with case studies /
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245 1 0 $a Applied linear regression for business analytics with R $h [electronic resource] : $b a practical guide to data science with case studies / $c by Daniel P. McGibney.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xvii, 276 p. : $b ill., digital ; $c 24 cm.
490 1 $a International series in operations research & management science, $x 2214-7934 ; $v v. 337
505 0 $a 1. Introduction -- 2. Basic Statistics and Functions using R -- 3. Regression Fundamentals -- 4. Simple Linear Regression -- 5. Multiple Regression -- 6. Estimation Intervals and Analysis of Variance -- 7. Predictor Variable Transformations -- 8. Model Diagnostics -- 9. Variable Selection.
520 $a Applied Linear Regression for Business Analytics with R introduces regression analysis to business students using the R programming language with a focus on illustrating and solving real-time, topical problems. Specifically, this book presents modern and relevant case studies from the business world, along with clear and concise explanations of the theory, intuition, hands-on examples, and the coding required to employ regression modeling. Each chapter includes the mathematical formulation and details of regression analysis and provides in-depth practical analysis using the R programming language.
650 0 $a Business $x Data processing. $3 527441
650 0 $a R (Computer program language) $3 784593
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650 2 4 $a IT in Business. $3 2114922
650 2 4 $a Business Analytics. $3 3592362
650 2 4 $a Statistics and Computing. $3 3594429
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830 0 $a International series in operations research & management science ; $v v. 337. $3 3661415
856 4 0 $u https://doi.org/10.1007/978-3-031-21480-6
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