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Statistical Application Development with R and Python - Second Edition

You're reading from  Statistical Application Development with R and Python - Second Edition

Product type Book
Published in Aug 2017
Publisher
ISBN-13 9781788621199
Pages 432 pages
Edition 2nd Edition
Languages
Toc

Table of Contents (19) Chapters close

Statistical Application Development with R and Python - Second Edition
Credits
About the Author
Acknowledgment
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Data Characteristics 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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