Statistical application development with R and python : power of statistics using R and python / Prabhanjan Narayanachar Tattar.

Format:

Book

Author/Creator:

Tattar, Prabhanjan Narayanachar, author.

Language:

English

Subjects (All):

Application software--Development.

Application software.

R (Computer program language).

Physical Description:

1 online resource (405 pages) : illustrations (some color)

Edition:

Second edition.

Place of Publication:

Birmingham, England ; Mumbai, [India] : Packt, 2017.

System Details:

text file

Summary:

Software Implementation Illustrated with R and Python About This Book Learn the nature of data through software which takes the preliminary concepts right away using R and Python. Understand data modeling and visualization to perform efficient statistical analysis with this guide. Get well versed with techniques such as regression, clustering, classification, support vector machines and much more to learn the fundamentals of modern statistics. Who This Book Is For If you want to have a brief understanding of the nature of data and perform advanced statistical analysis using both R and Python, then this book is what you need. No prior knowledge is required. Aspiring data scientist, R users trying to learn Python and vice versa What You Will Learn Learn the nature of data through software with preliminary concepts right away in R Read data from various sources and export the R output to other software Perform effective data visualization with the nature of variables and rich alternative options Do exploratory data analysis for useful first sight understanding building up to 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 for forming 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 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 e...

Contents:

Cover

Copyright

Credits

About the Author

Acknowledgment

About the Reviewers

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: Data Characteristics

Questionnaire and its components

Understanding the data characteristics in an R environment

Experiments with uncertainty in computer science

Installing and setting up R

Using R packages

RSADBE - the books R package

Python installation and setup

Using pip for packages

IDEs for R and Python

The companion code bundle

Discrete distributions

Discrete uniform distribution

Binomial distribution

Hypergeometric distribution

Negative binomial distribution

Poisson distribution

Continuous distributions

Uniform distribution

Exponential distribution

Normal distribution

Summary

Chapter 2: Import/Export Data

Packages and settings - R and Python

Understanding data.frame and other formats

Constants, vectors, and matrices

Time for action - understanding constants, vectors, and basic arithmetic

What just happened?

Doing it in Python

Time for action - matrix computations

The list object

Time for action - creating a list object

The data.frame object

Time for action - creating a data.frame object

Have a go hero

The table object

Time for action - creating the Titanic dataset as a table object

Using utils and the foreign packages

Time for action - importing data from external files

Importing data from MySQL

Exporting data/graphs

Exporting R objects

Exporting graphs

Time for action - exporting a graph

Managing R sessions.

Time for action - session management

Pop quiz

Chapter 3: Data Visualization

Visualization techniques for categorical data

Bar chart

Going through the built-in examples of R

Time for action - bar charts in R

Dot chart

Time for action - dot charts in R

Spine and mosaic plots

Time for action - spine plot for the shift and operator data

Time for action - mosaic plot for the Titanic dataset

Pie chart and the fourfold plot

Visualization techniques for continuous variable data

Boxplot

Time for action - using the boxplot

Histogram

Time for action - understanding the effectiveness of histograms

Scatter plot

Time for action - plot and pairs R functions

Pareto chart

A brief peek at ggplot2

Time for action - qplot

Time for action - ggplot

Chapter 4: Exploratory Analysis

Essential summary statistics

Percentiles, quantiles, and median

Hinges

Interquartile range

Time for action - the essential summary statistics for The Wall dataset

Techniques for exploratory analysis

The stem-and-leaf plot

Time for action - the stem function in play

Letter values

Data re-expression

Bagplot - a bivariate boxplot

Time for action - the bagplot display for multivariate datasets.

What just happened?

Resistant line

Time for action - resistant line as a first regression model

Smoothing data

Time for action - smoothening the cow temperature data

Median polish

Time for action - the median polish algorithm

Chapter 5: Statistical Inference

Maximum likelihood estimator

Visualizing the likelihood function

Time for action - visualizing the likelihood function

Finding the maximum likelihood estimator

Using the fitdistr function

Time for action - finding the MLE using mle and fitdistr functions

Confidence intervals

Time for action - confidence intervals

Hypothesis testing

Binomial test

Time for action - testing probability of success

Tests of proportions and the chi-square test

Time for action - testing proportions

Tests based on normal distribution - one sample

Time for action - testing one-sample hypotheses

Tests based on normal distribution - two sample

Time for action - testing two-sample hypotheses

Chapter 6: Linear Regression Analysis

The essence of regression

The simple linear regression model

What happens to the arbitrary choice of parameters?

Time for action - the arbitrary choice of parameters

Building a simple linear regression model

Time for action - building a simple linear regression model

Have a go hero.

ANOVA and the confidence intervals

Time for action - ANOVA and the confidence intervals

Model validation

Time for action - residual plots for model validation

Multiple linear regression model

Averaging k simple linear regression models or a multiple linear regression model

Time for action - averaging k simple linear regression models

Building a multiple linear regression model

Time for action - building a multiple linear regression model

The ANOVA and confidence intervals for the multiple linear regression model

Time for action - the ANOVA and confidence intervals for the multiple linear regression model

Useful residual plots

Time for action - residual plots for the multiple linear regression model

Regression diagnostics

Leverage points

Influential points

DFFITS and DFBETAS

The multicollinearity problem

Time for action - addressing the multicollinearity problem for the gasoline data

Model selection

Stepwise procedures

The backward elimination

The forward selection

The stepwise regression

Criterion-based procedures

Time for action - model selection using the backward, forward, and AIC criteria

Chapter 7: Logistic Regression Model

The binary regression problem

Time for action - limitation of linear regression model

Probit regression model

Time for action - understanding the constants

Logistic regression model

Time for action - fitting the logistic regression model.

Hosmer-Lemeshow goodness-of-fit test statistic

Time for action - Hosmer-Lemeshow goodness-of-fit statistic

Model validation and diagnostics

Residual plots for the GLM

Time for action - residual plots for logistic regression model

Influence and leverage for the GLM

Time for action - diagnostics for the logistic regression

Receiving operator curves

Time for action - ROC construction

Logistic regression for the German credit screening dataset

Time for action - logistic regression for the German credit dataset

Chapter 8: Regression Models with Regularization

The overfitting problem

Time for action - understanding overfitting

Regression spline

Basis functions

Piecewise linear regression model

Time for action - fitting piecewise linear regression models

Natural cubic splines and the general B-splines

Time for action - fitting the spline regression models

Ridge regression for linear models

Protecting against overfitting

Time for action - ridge regression for the linear regression model

Ridge regression for logistic regression models

Time for action - ridge regression for the logistic regression model

Another look at model assessment

Time for action - selecting iteratively and other topics

Summary.

Chapter 9: Classification and Regression Trees.

Notes:

Includes index.

Includes bibliographical references and index.

Description based on online resource; title from PDF title page (ebrary, viewed September 26, 2017).

OCLC:

1004966445