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Applied linear regression for business analytics with Ra practical guide to data science with case studies /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applied linear regression for business analytics with Rby 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.
Contained By:	Springer Nature eBook
Subject:	BusinessData processing.
Online resource:	https://doi.org/10.1007/978-3-031-21480-6
ISBN:	9783031214806$q(electronic bk.)

Applied linear regression for business analytics with Ra 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. 140..

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$q(electronic bk.)

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

199085
Business
--Data processing.

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

Dewey Class. No.: 650.0285

Applied linear regression for business analytics with Ra practical guide to data science with case studies /
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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.
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