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

Maximum likelihood estimator


Let us consider the discrete probability distributions as seen in the Discrete distributions section of Chapter 1, Data Characteristics. We saw that a binomial distribution is characterized by the parameters in n and p, the Poisson distribution by , and so on. Here, the parameters completely determine the probabilities of the x values. However, when the parameters are unknown, which is the case in almost all practical problems, we collect data for the random experiment and try to infer about the parameters. This is essentially inductive reasoning, and the subject of Statistics is essentially inductive driven as opposed to the deductive reasoning of Mathematics. This forms the core difference between the two beautiful subjects. Assume that we have n observations X1, X2,…, Xn from an unknown probability distribution , where may be a scalar or a vector whose values are not known. Let us consider a few important definitions that form the core of statistical inference...