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Introduction to R for Business Intelligence

You're reading from   Introduction to R for Business Intelligence Profit optimization using data mining, data analysis, and Business Intelligence

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Product type Paperback
Published in Aug 2016
Publisher Packt
ISBN-13 9781785280252
Length 228 pages
Edition 1st Edition
Languages
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Author (1):
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Jay Gendron Jay Gendron
Author Profile Icon Jay Gendron
Jay Gendron
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Table of Contents (13) Chapters Close

Preface 1. Extract, Transform, and Load FREE CHAPTER 2. Data Cleaning 3. Exploratory Data Analysis 4. Linear Regression for Business 5. Data Mining with Cluster Analysis 6. Time Series Analysis 7. Visualizing the Datas Story 8. Web Dashboards with Shiny A. References
B. Other Helpful R Functions C. R Packages Used in the Book
D. R Code for Supporting Market Segment Business Case Calculations

Chapter 4.  Linear Regression for Business

Linear regression is a powerful tool that enables the business analyst to perform data analytics and business intelligence. Linear regression is a statistical technique to represent relationships between two or more variables using a linear equation. You can use linear regression to predict an outcome, given some input. This chapter covers five topics that will give you the skills to use this technique in your analyses:

  • Understanding linear regression
  • Checking model assumptions
  • Using a simple linear regression
  • Refining data for simple linear regression
  • Introducing multiple linear regression

Together, these topics provide a building-block approach to not only teach you the skills to build linear models, but also shape your analytical thinking. Using this approach with other datasets will help you to keep practicing these skills.

Note

Use case: The Marketing Dataset

The marketing manager has asked you to analyze marketing data to provide...

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