Applied Linear Regression for Business Analytics with R : A Practical Guide to Data Science with Case Studies

by Daniel P. McGibney

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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Year of publication:	2023
Authors:	McGibney, Daniel P.
Publisher:	2023.: Cham : Springer International Publishing 2023.: Cham : Imprint: Springer
Subject:	Data Science \| Business Analytics \| Applied Regression \| Regression Analysis \| R Programming \| Big Data \| Business Mathematics \| Linear Models \| Machine Learning \| Management-Informationssystem \| Management information system \| Regressionsanalyse \| Regression analysis \| Big data \| Statistische Methode \| Statistical method \| Künstliche Intelligenz \| Artificial intelligence \| Unternehmensanalyse \| Business analysis \| Schätztheorie \| Estimation theory

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Extent:	1 Online-Ressource (XVII, 276 p. 86 illus., 53 illus. in color.)
Series:	International Series in Operations Research & Management Science ; 337
Type of publication:	Book / Working Paper
Language:	English
ISBN:	978-3-031-21480-6 ; 978-3-031-21479-0 ; 978-3-031-21481-3 ; 978-3-031-21482-0
Other identifiers:	10.1007/978-3-031-21480-6 [DOI]
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10014286486