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

Pareto chart


The Pareto rule, also known as the 80-20 rule or the law of vital few, says that approximately 80 percent of the defects are due to 20 percent of the causes. It is important as it can identify 20 percent vital causes whose elimination annihilates 80 percent of the defects. The qcc package contains the function pareto.chart, which helps in generating the Pareto chart. We will give a simple illustration of this chart.

The Pareto chart is a display of the cause frequencies along two axes. Suppose that we have 10 causes C1 to C10 that have occurred with defect counts 5, 23, 7, 41, 19, 4, 3, 4, 2, and 1. Causes 2, 4, and 5 have high frequencies (dominating?) and other causes look a bit feeble. Now, let us sort these causes by decreasing the order and obtain their cumulative frequencies. We will also obtain their cumulative percentages:

> Cause_Freq <- c(5, 23, 7, 41, 19, 4, 3, 4, 2, 1)
> names(Cause_Freq) <- paste("C",1:10,sep="")
> Cause_Freq_Dec <- sort(Cause_Freq...