A User's Guide to Business Analytics

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

Probability
Basic Set Theory
The Classical Definition of Probability
Counting Rules
Axiomatic Definition of Probability
Conditional Probability and Independence
The Bayes Theorem
Comprehensive Example
Appendix
Suggested Further Reading

Random Variables and Probability Distributions
Discrete and Continuous Random Variables
Some Special Discrete Distributions
Distribution Functions
Bivariate and Multivariate Distributions
Expectation
Appendix
Suggested Further Reading

Continuous Random Variables
The PDF and the CDF
Special Continuous Distributions
Expectation
The Normal Distribution
Continuous Bivariate Distributions
Independence
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
Summary
Appendix
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
Appendix
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
Interaction
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

Biography

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.