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

Summary


Median and its variants form the core measures of EDA and you would have got a hang of it by the first section. The visualization techniques of EDA also compose more than just the stem-and-leaf plot, letter values, and bagplot. As EDA is basically about your attitude and approach, it is important to realize that you can (and should) use any method that is instinctive and appropriate for the data on hand. We have also built our first regression model in the resistant line and seen how robust it is to the outliers. Smoothing data and median polish are also advanced EDA techniques that the reader is acquainted with from their respective sections.

EDA is exploratory in nature and its findings may need further statistical validations. The next chapter on statistical inference addresses what Tukey calls, confirmatory analysis. Especially, we look at techniques that give good point estimates of the unknown parameters. This is then backed with further techniques such as goodness-of-fit and...