A User's Guide to Business Analytics: 1st Edition (Hardback) book cover

A User's Guide to Business Analytics

1st Edition

By Ayanendranath Basu, Srabashi Basu

Chapman and Hall/CRC

384 pages | 114 B/W Illus.

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Hardback: 9781466591653
pub: 2016-10-20
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pub: 2016-08-19
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A User's Guide to Business Analytics provides a comprehensive discussion of statistical methods useful to the business analyst. Methods are developed from a fairly basic level to accommodate readers who have limited training in the theory of statistics. A substantial number of case studies and numerical illustrations using the R-software package are provided for the benefit of motivated beginners who want to get a head start in analytics as well as for experts on the job who will benefit by using this text as a reference book.

The book is comprised of 12 chapters. The first chapter focuses on business analytics, along with its emergence and application, and sets up a context for the whole book. The next three chapters introduce R and provide a comprehensive discussion on descriptive analytics, including numerical data summarization and visual analytics. Chapters five through seven discuss set theory, definitions and counting rules, probability, random variables, and probability distributions, with a number of business scenario examples. These chapters lay down the foundation for predictive analytics and model building.

Chapter eight deals with statistical inference and discusses the most common testing procedures. Chapters nine through twelve deal entirely with predictive analytics. The chapter on regression is quite extensive, dealing with model development and model complexity from a user’s perspective. A short chapter on tree-based methods puts forth the main application areas succinctly. The chapter on data mining is a good introduction to the most common machine learning algorithms. The last chapter highlights the role of different time series models in analytics. In all the chapters, the authors showcase a number of examples and case studies and provide guidelines to users in the analytics field.

Table of Contents

What Is Analytics?

The Emergence and Application of Analytics

Similarities with and Dissimilarities from Classical Statistical Analysis

Theory versus Computational Power

Fact versus Knowledge: Report versus Prediction

Actionable Insight

Suggested Further Reading

Introducing R—An Analytics Software

Basic System of R

Reading, Writing, and Extracting Data in R

Statistics in R

Graphics in R

Further Notes about R

Suggested Further Reading

Reporting Data

What Is Data?

Types of Data

Data Collection and Presentation

Reporting Current Status

Measures of Association for Categorical Variables

Suggested Further Reading

Statistical Graphics and Visual Analytics

Univariate and Bivariate Visualization

Multivariate Visualization

Mapping Techniques

Scopes and Challenges of Visualization

Suggested Further Reading


Basic Set Theory

The Classical Definition of Probability

Counting Rules

Axiomatic Definition of Probability

Conditional Probability and Independence

The Bayes Theorem

Comprehensive Example


Suggested Further Reading

Random Variables and Probability Distributions

Discrete and Continuous Random Variables

Some Special Discrete Distributions

Distribution Functions

Bivariate and Multivariate Distributions



Suggested Further Reading

Continuous Random Variables

The PDF and the CDF

Special Continuous Distributions


The Normal Distribution

Continuous Bivariate Distributions


The Bivariate Normal Distribution

Sampling Distributions

The Central Limit Theorem

Sampling Distributions Arising from the Normal

Random Samples from Two Independent Normal Distributions

Normal Q-Q Plots



Suggested Further Reading

Statistical Inference

Inference about a Single Mean

Single Population Mean with Unknown Variance

Two Sample t-test: Independent Samples

Two Sample t-test: Dependent (Paired) Samples

Analysis of Variance

Chi-Square Tests

Inference about Proportions


Suggested Further Reading

Regression for Predictive Model Building

Simple Linear Regression

Multiple Linear Regression

ANOVA for Multiple Linear Regression

Hypotheses of Interest in Multiple Linear Regression


Regression Diagnostics

Regression Model Building

Other Regression Techniques

Logistic Regression

Interpreting Logistic Regression Model

Interpretation and Inference for Logistic Regression

Goodness of Fit for the Logistic Regression Model

Hosmer-Lemeshow Statistics

Classification Table and ROC Curve

Suggested Further Reading

Decision Trees

Algorithm for Tree-Based Methods

Impurity Measures

Pruning a Tree

Aggregation Method: Bagging

Random Forest

Variable Importance

Decision Tree and Interaction among Predictors

Suggested Further Reading

Data Mining and Multivariate Methods

Dimension Reduction Technique: Principal Component Analysis

Factor Analysis

Classification Problem

Discriminant Analysis

Clustering Problem

Suggested Further Reading

Modeling Time Series Data for Forecasting

Characteristics and Components of Time Series Data

Time Series Decomposition

Autoregression Models

Forecasting Time Series Data

Other Time Series

Suggested Further Reading

About the Authors

Ayanendranath Basu earned his PhD in statistics from The Pennsylvania State University in 1991, under the guidance of late Professor Bruce. G. Lindsay. After spending four years at the Department of Mathematics, University of Texas at Austin, as an assistant professor, he joined the Indian Statistical Institute in 1995. Currently, Dr. Basu is a professor of the Interdisciplinary Statistical Research Unit (ISRU), ISI-Kolkata. His research interests lie mainly in the following areas: minimum distance inference, robust inference, multivariate analysis, and biostatistics.

Srabashi Basu earned her PhD in statistics from The Pennsylvania State University in 1992. After spending several years in University of Texas Health Science Center in San Antonio, she joined Indian Statistical Institute in 1995. Since 2006, Dr. Basu is working as an analytics specialist and independent consultant. She has extensive applied research publications to her credit. She also works as a corporate trainer in various areas of predictive analytics and machine learning. Dr. Basu has been an online instructor for Penn State Statistics World Campus courses since 2009. She also has developed online course materials in statistics, business analytics, R, and SAS.

Subject Categories

BISAC Subject Codes/Headings:
BUSINESS & ECONOMICS / Management Science
COMPUTERS / Database Management / Data Mining
MATHEMATICS / Probability & Statistics / General