A user’s guide to business analytics

✍ Scribed by Basu, Ayanennath.; Basu, Srabashi

• Publisher: Chapman and Hall/CRC
• Year: 2016
• Tongue: English
• Leaves: 400
• Category: Library

✦ Synopsis

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

Content: Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
1: What Is Analytics?
1.1 Emergence and Application of Analytics
1.2 Comparison with Classical Statistical Analysis
1.3 Theory versus Computational Power 1.4 Fact versus Knowledge: Report versus Prediction 1.5 Actionable Insight
1.6 Suggested Further Reading
2: Introducing R-An Analytics Software
2.1 Basic System of R
2.2 Reading, Writing and Extracting Data in R
2.3 Statistics in R
2.4 Graphics in R
2.5 Further Notes about R 2.6 Suggested Further Reading 3: Reporting Data
3.1 What Is Data?
3.2 Types of Data
3.2.1 Qualitative versus Quantitative Variable
3.2.2 Discrete versus Continuous Data
3.2.3 Interval versus Ratio Type Data
3.3 Data Collection and Presentation
3.4 Reporting Current Status 3.4.1 Measures of Central Tendency 3.4.2 Measures of Dispersion
3.4.3 Measures of Association
3.5 Measures of Association for Categorical Variables
3.6 Suggested Further Reading
4: Statistical Graphics and Visual Analytics
4.1 Univariate and Bivariate Visualization
4.1.1 Univariate Visualization 4.1.2 Bivariate Visualization 4.2 Multivariate Visualization
4.3 Mapping Techniques
4.4 Scopes and Challenges of Visualization
4.5 Suggested Further Reading
5: Probability
5.1 Basic Set Theory
5.2 Classical Definition of Probability
5.3 Counting Rules

✦ Subjects

Commercial statistics.;R (Computer program language);Data mining.;BUSINESS & ECONOMICS / Industrial Management;BUSINESS & ECONOMICS / Management;BUSINESS & ECONOMICS / Management Science;BUSINESS & ECONOMICS / Organizational Behavior