Book Image

Statistical Application Development with R and Python - Second Edition

Book Image

Statistical Application Development with R and Python - Second Edition

Overview of this book

Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions. This book explores statistical concepts along with R and Python, which are well integrated from the word go. Almost every concept has an R code going with it which exemplifies the strength of R and applications. The R code and programs have been further strengthened with equivalent Python programs. Thus, you will first understand the data characteristics, descriptive statistics and the exploratory attitude, which will give you firm footing of data analysis. Statistical inference will complete the technical footing of statistical methods. Regression, linear, logistic modeling, and CART, builds the essential toolkit. This will help you complete complex problems in the real world. You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python. The data analysis journey begins with exploratory analysis, which is more than simple, descriptive, data summaries. You will then apply linear regression modeling, and end with logistic regression, CART, and spatial statistics. By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.
Table of Contents (19 chapters)
Statistical Application Development with R and Python - Second Edition
Credits
About the Author
Acknowledgment
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

Visualization techniques for categorical data


In Chapter 1, Data Characteristics, we came across many variables whose outcomes are categorical in nature. Gender, Car_Model, Minor_Problems, Major_Problems, and Satisfaction_Rating are examples of categorical data. In a software product development cycle, various issues or bugs are raised at different severity levels such as Minor and Show Stopper. Visualization methods for the categorical data require special attention and techniques, and the goal of this section is to aid the reader with some useful graphical tools.

In this section, we will mainly focus on the dataset related to bugs, which are of primary concern for any software engineer. The source of the datasets is http://bug.inf.usi.ch/ and the reader is advised to check the website before proceeding further in this section. We will begin with the software system Eclipse JDT Core, and the details for this system may be found at http://www.eclipse.org/jdt/core/index.php. The files for...