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

Splitting the data


In the earlier discussion, we saw that partitioning the dataset can be of great benefit in reducing the noise in the data. The question is how does one begin with it? The explanatory variables can be discrete or continuous. We will begin with the continuous (numeric objects in R) variables.

For a continuous variable, the task is a bit simpler. First, identify the unique distinct values of the numeric object. Let us say, for example, that the distinct values of a numeric object, say height in cms, are 160, 165, 170, 175, and 180. The data partitions are then obtained as follows:

  • data[Height<=160,], data[Height>160,]

  • data[Height<=165,], data[Height>165,]

  • data[Height<=170,], data[Height>170,]

  • data[Height<=175,], data[Height>175,]

The reader should try to understand the rationale behind the code, and certainly this is just an indicative one.

Now, we consider the discrete variables. Here, we have two types of variables, namely categorical and ordinal...