Applied linear regression for business analytics with R : a practical guide to data science with case studies / Daniel P. McGibney.
McGibney, Daniel P., author.
2023
HD30.2 .M34 2023
Available Online

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Title
Applied linear regression for business analytics with R : a practical guide to data science with case studies / Daniel P. McGibney.
Author
McGibney, Daniel P., author.
ISBN
9783031214806 (electronic bk.)
3031214803 (electronic bk.)
303121479X
9783031214790
DOI
https://doi.org/10.1007/978-3-031-21480-6
Published
Cham, Switzerland : Springer, [2023]
Language
English
Description
1 online resource.
Item Number
10.1007/978-3-031-21480-6 doi
Call Number
HD30.2 .M34 2023
Dewey Decimal Classification
650.0285
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Source of Description
Description based on online resource; title from digital title page (viewed on July 18, 2023).
Series
International series in operations research & management science ; 337.
Available in Other Form
Print version: 9783031214790
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Online Access
Record Appears in
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All Resources
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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