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

Model validation and diagnostics


In the previous chapter, we saw the utility of residual techniques. A similar technique is also required for the logistic regression model and we will develop these methods for the logistic regression model in this section.

Residual plots for the GLM

In the case of linear regression model, we had explored the role of residuals for the purpose of model validation. In the context of logistic regression, actually GLM, we have five different types of residuals for the same purpose:

  • Response residual: The difference between the actual values and the fitted values is the response residual, that is, , and in particular it is if yi = 1 and for yi = 0.
  • Deviance residual: For an observation i, the deviance residual is the signed square root of the contribution of the observation to the sum of the model deviance. That is, it is given by:

Where the sign is positive if , and negative otherwise, and is the predicted probability of success.

  • Pearson residual: The Pearson...