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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

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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