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

The binary regression problem


Consider the problem of modeling the completion of a stat course by students based on their Scholastic Assessment Test in the subject of mathematics SAT-M scores at the time of their admission. After the completion of the final exams we know which students successfully completed the course and which of them failed. Here, the output pass/fail may be represented by a binary number 1/0. It may be fairly said that higher the SAT-M scores at the time of admission to the course, the more likelihood of the candidate completing the course. This problem has been discussed in detail in Johnson and Albert (1999) and Tattar, et. al. (2013).

Let us begin by denoting the pass/fail indicator by Y and the entry SAT-M score by X. Suppose that we have n pairs of observations on the students' scores and their course completion results. We can build the simple linear regression model for the probability of course completion pi = P(Yi = 1) as a function of the SAT-M score with ....