# R Statistical Application Development by Example Beginner's Guide

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## Book Details

## About This Book

- A self-learning guide for the user who needs statistical tools for understanding uncertainty in computer science data
- Essential descriptive statistics, effective data visualization, and efficient model building
- Every method explained through real data sets enables clarity and confidence for unforeseen scenarios

## Who This Book Is For

You will begin learning the first concepts of statistics in R which is vital in this fast paced era and it is also a bargain as you do not need to do a preliminary course on the subject.

## Table of Contents

## What You Will Learn

- Learn the nature of data through software which takes the preliminary concepts right away in R
- Read data from various sources and export the R output to other software
- Perform effective data visualization which respects the nature of variables and with rich alternative options
- Do exploratory data analysis for useful first understanding which builds up the right attitude towards effective inference
- Learn statistical inference through simulation combining the classical inference and modern computational power
- Delve deep into regression models such as linear and logistic for continuous and discrete regressands which form the fundamentals of modern statistics
- Introduce yourself to CART – a machine learning tool which is very useful when the data has an intrinsic nonlinearity

## In Detail

"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.

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.

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.

True to the title R Statistical Application Development by Example Beginner’s Guide, the reader will enjoy the examples and R software.