Book Image

R Statistical Application Development by Example Beginner's Guide

By : Prabhanjan Narayanachar Tattar
Book Image

R Statistical Application Development by Example Beginner's Guide

By: Prabhanjan Narayanachar Tattar

Overview of this book

<p>"R Statistical Application Development by Example Beginner’s Guide" explores statistical concepts and the R software, which are well integrated from the word go. This demarcates the separate learning of theory and applications and hence the title begins with “R Statistical …”. Almost every concept has an R code going with it which exemplifies the strength of R and applications. Thus, the reader first understands the data characteristics, descriptive statistics, and the exploratory attitude which gives the first firm footing of data analysis. Statistical inference and the use of simulation which makes use of the computational power complete the technical footing of statistical methods. Regression modeling, linear, logistic, and CART, builds the essential toolkit which helps the reader complete complex problems in the real world.<br /><br />The reader 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.<br /><br />The data analysis journey begins with exploratory analysis, which is more than simple descriptive data summaries, and then takes the traditional path up to linear regression modeling, and ends with logistic regression, CART, and spatial statistics.<br /><br />True to the title R Statistical Application Development by Example Beginner’s Guide, the reader will enjoy the examples and R software.</p>
Table of Contents (18 chapters)
R Statistical Application Development by Example Beginner's Guide
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
References
Index

Regression diagnostics


In the Useful residual plots subsection, we saw how outliers may be identified using the residual plots. If there are outliers, we need to ask the following questions:

  • Is the observation an outlier due to an anomalous value in one or more covariate values?

  • Is the observation an outlier due to an extreme output value?

  • Is the observation an outlier because of both the covariate and output values being extreme values?

The distinction in the nature of an outlier is vital as one needs to be sure of its type. The techniques for outlier identification are certainly different as is their impact. If the outlier is due to the covariate value, the observation is called a leverage point, and if it is due to the y value, we call it an influential point. The rest of the section is for the exact statistical technique for such an outlier identification.

Leverage points

As noted, a leverage point has an anomalous x value. The leverage points may be theoretically proved not to impact the...