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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

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Product type Paperback
Published in Aug 2017
Publisher
ISBN-13 9781788621199
Length 432 pages
Edition 2nd Edition
Languages
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Author (1):
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Prabhanjan Narayanachar Tattar Prabhanjan Narayanachar Tattar
Author Profile Icon Prabhanjan Narayanachar Tattar
Prabhanjan Narayanachar Tattar
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Toc

Table of Contents (12) Chapters Close

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

Techniques for exploratory analysis


We will be studying the following techniques:

  • The stem-and-leaf plot

  • Letter values

  • Data re-expression

  • Bagplot—a bivariate boxplot

  • Resistant line

  • Smoothing data

  • Median polish

The stem-and-leaf plot

The stem-and-leaf plot is considered as one of the seven important tools of Statistical Process Control (SPC); refer to Montgomery (2005). It is a bit similar in nature to the histogram plot.

The stem-and-leaf plot is an effective method of displaying data in a (partial) tree form. Here, each datum is split into two parts: the stem part and the leaf part. In general, the last digit of a datum forms the leaf part; the rest form the stem. Now, consider a datum 235. If the split criteria is the units place, the stem and leaf parts here will be respectively 23 and 5; if it is tens, then 2 and 3; and finally if it is hundreds, it will be 0 and 2. The left-hand side of the split datum is called the leading digits and the right-hand side the trailing digits.

In the next step,...

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