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Statistical Application Development with R and Python

You're reading from Statistical Application Development with R and Python Develop applications using data processing, statistical models, and CART

Product type Paperback

Published in Aug 2017

Publisher

ISBN-13 9781788621199

Length 432 pages

Edition 2nd Edition

Languages

Python

Concepts

Application Development

Author (1):

Prabhanjan Narayanachar Tattar

View More author details

Table of Contents (12) Chapters

Preface

1. Data Characteristics FREE CHAPTER

2. Import/Export Data

3. Data Visualization

4. Exploratory Analysis

5. Statistical Inference

6. Linear Regression Analysis

7. Logistic Regression Model

8. Regression Models with Regularization

9. Classification and Regression Trees

10. CART and Beyond

Index

Chapter 4. Exploratory Analysis

Tukey (1977) in his benchmark book Exploratory Data Analysis, abbreviated popularly as EDA, explains the best about the "best methods" as:

We do not guarantee to introduce you to the "best" tools, particularly since we are not sure that there can be unique bests.

The goal of this chapter is to emphasize on Exploratory Data Analysis (EDA) and its strength.

In the previous chapter, we have seen visualization techniques for data of different characteristics. Analytical insight is also important and this chapter considers EDA techniques. Furthermore, the more popular measures include the mean, standard error, and so on. It has been proved many times that mean has several drawbacks; one being that it is very sensitive to outliers/extremes. Thus, in exploratory analysis the focus is on measures that are robust to the extremes. Many techniques considered in this chapter are discussed in more detail by Velleman and Hoaglin (1981), and an...

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