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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 3. Exploratory Data Analysis

One way to learn new things is through discovery. Exploratory data analysis is a term attributed to the statistician John Tukey in a book of the same name (Tukey, 1977). Exploratory data analysis means examining a dataset to discover its underlying characteristics with an emphasis on visualization. It helps you during analysis design to determine if you should gather more data, suggest hypotheses to test, and identify models to develop. In this chapter, we will cover the following four topics related to exploratory data analysis:

  • Understanding exploratory data analysis
  • Analyzing a single data variable
  • Analyzing two variables together
  • Exploring multiple variables simultaneously

You will learn common techniques that statisticians and analysts use to characterize data. These include tabular and graphical methods to explore the dataset. There are many interesting things to discover in a dataset, but in business or science you are exploring to determine...

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