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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

Visualization techniques for continuous variable data


Continuous variables have a different structure and, hence, we need specialized methods for displaying them. Fortunately, many popular graphical techniques are suited very well for continuous variables. As the continuous variables can arise from different phenomena, we consider many techniques in this section. The graphical methods discussed in this section may also be considered as a part of the next chapter on exploratory analysis.

Boxplot

The boxplot is based on five points: minimum, lower quartile, median, upper quartile, and maximum. The median forms the thick line near the middle of the box, and the lower and upper quartiles complete the box. The lower and upper quartiles along with the median, which is the second quartile, divide the data into four regions, with each containing equal number of observations. The median is the middle-most value among the data sorted in the increasing (decreasing) order of magnitude. On similar lines...

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