Includes bibliographical references pg 705.

Preface.
Acknowledgments.

1. Introduction to Business Analytics.
1.1 Introduction to Business Analytics.
1.2 Why Analytics.
1.3 Business Analytics: The Science of Data-Driven Decision Making.
1.4 Descriptive Analytics.
1.5 Predictive Analytics.
1.6 Prescriptive Analytics.
1.7 Descriptive, Predictive and Prescriptive Analytics Techniques.
1.8 Big Data Analytics.
1.9 Web and Social Media Analytics.
1.10 Machine Learning Algorithms.
1.11 Framework for Data-Driven Decision Making.
1.12 Analytics Capability Building.
1.13 Roadmap for Analytics Capability Building.
1.14 Challenges in Data-Driven Decision Making and Future.
1.15 Organization of the Book.
2. Descriptive Analytics.
2.1 Introduction to Descriptive Analytics.
2.2 Data Types and Scales.
2.3 Types of Data Measurement Scales.
2.4 Population and Sample.
2.6 Percentile, Decile and Quartile.
2.7 Measures of Variation.
2.8 Measures of Shape − Skewness and Kurtosis.
2.9 Data Visualization.
3. Introduction to Probability.
3.1 Introduction to Probability Theory.
3.2 Probability Theory – Terminology.
3.3 Fundamental Concepts in Probability – Axioms of Probability.
3.4 Application of Simple Probability Rules – Association Rule Learning.
3.5 Bayes’ Theorem.
3.6 Random Variables.
3.7 Probability Density Function (PDF) and Cumulative Distribution Function (CDF) of a Continuous Random Variable.
3.8 Binomial Distribution.
3.9 Poisson Distribution.
3.10 Geometric Distribution.
3.11 Parameters of Continuous Distributions.
3.12 Uniform Distribution.
3.13 Exponential Distribution.
3.15 Chi-Square Distribution.
3.16 Student’s t-Distribution.
3.17 F-Distribution.
4. Sampling and Estimation.
4.1 Introduction to Sampling.
4.2 Population Parameters and Sample Statistic.
4.3 Sampling.
4.4 Probabilistic Sampling.
4.5 Non-Probability Sampling.
4.6 Sampling Distribution.
4.7 Central Limit Theorem (CLT).
4.8 Sample Size Estimation for Mean of the Population.
4.9 Estimation of Population Parameters.
4.10 Method of Moments.
4.11 Estimation of Parameters Using Method of Moments.
4.12 Estimation of Parameters Using Maximum Likelihood Estimation.
5. Confidence Intervals.
5.1 Introduction to Confidence Interval.
5.2 Confidence Interval for Population Mean.
5.3 Confidence Interval for Population Proportion.
5.4 Confidence Interval for Population Mean When Standard Deviation is Unknown.
5.5 Confidence Interval for Population Variance.
6. Hypothesis Testing.
6.1 Introduction to Hypothesis Testing.
6.2 Setting Up a Hypothesis Test.
6.3 One-Tailed and Two-tailed Test.
6.4 Type I Error, Type II Error and Power of The Hypothesis Test.
6.5 Hypothesis Testing for Population mean with Known Variance: Z-Test.
6.6 Hypothesis Testing for Population Proportion: Z-Test for Proportion.
6.7 Hypothesis Test for Population mean under Unknown Population Variance: t-Test.
6.8 Paired Sample t-Test.
6.9 Comparing Two Populations: Two-Sample Z- and t-Test.
6.10 Hypothesis Test for Difference in Population Proportion under Large Samples: Two-Sample Z-Test for Proportions.
6.11 Effect Size: Cohen’s D.
6.12 Hypothesis Test for Equality of Population Variances.
6.13 Non-Parametric Tests: Chi-Square Tests.
7. Analysis of Variance.
7.1 Introduction to Analysis of Variance (ANOVA).
7.2 Multiple t-Tests for Comparing Several Means.
7.3 One-way Analysis of Variance (ANOVA).
7.4 Two-Way Analysis of Variance (ANOVA).
8. Correlation Analysis.
8.1 Introduction to Correlation.
8.2 Pearson Correlation Coefficient.
8.3 Spearman Rank Correlation.
8.4 Point Bi-Serial Correlation.
8.5 The Phi-coefficient.
9. Simple Linear Regression.
9.1 Introduction to Simple Linear Regression.
9.2 History of Regression–Francis Galton’s Regression Model.
9.3 Simple Linear Regression Model Building.
9.4 Estimation of Parameters Using Ordinary Least Squares.
9.5 Interpretation of Simple Linear Regression Coefficients.
9.6 Validation of the Simple Linear Regression Model.
9.7 Outlier Analysis.
9.8 Confidence Interval for Regression Coefficients b0 and b.
9.9 Confidence Interval for the Expected Value of Y for a Given X.
9.10 Prediction Interval for the Value of Y for a Given X.
10. Multiple Linear Regression.
10.1 Introduction.
10.2 Ordinary Least Squares Estimation for Multiple Linear Regression.
10.3 Multiple Linear Regression Model Building.
10.4 Part (Semi-Partial) Correlation and Regression Model Building.
10.5 Interpretation of MLR Coefficients − Partial Regression Coefficient.
10.6 Standardized Regression Co-efficient.
10.8 Validation of Multiple Regression Model.
10.9 Co-efficient of Multiple Determination (R-Square) and Adjusted R-Square.
10.10 Statistical Significance of Individual Variables in MLR – t-Test.
10.11 Validation of Overall Regression Model: F-Test.
10.12 Validation of Portions of a MLR Model – Partial F-Test.
10.13 Residual Analysis in Multiple Linear Regression.
10.14 Multi-Collinearity and Variance Inflation Factor.
10.15 Auto-correlation.
10.16 Distance Measures and Outliers Diagnostics.
10.17 Variable Selection in Regression Model Building (Forward, Backward, and Stepwise Regression).
10.18 Avoiding Overfitting: Mallows’s Cp.
10.19 Transformations.
11. Logistic Regression.
11.1 Introduction – Classification Problems.
11.2 Introduction to Binary Logistic Regression.
11.3 Estimation of Parameters in Logistic Regression.
11.4 Interpretation of Logistic Regression Parameters.
11.5 Logistic Regression Model Diagnostics.
11.6 Classification Table, Sensitivity, and Specificity.
11.7 Optimal Cut-Off Probability.
11.8 Variable Selection in Logistic Regression.
11.9 Application of Logistic Regression in Credit Rating.
11.10 Gain Chart and Lift Chart.
12. Decision Trees.
12.1 Decision Trees: Introduction.
12.2 Chi-Square Automatic Interaction Detection (CHAID).
12.3 Classification and Regression Tree.
12.4 Cost-Based Splitting Criteria.
12.5 Ensemble Method.
12.6 Random Forest.
13. Forecasting Techniques.
13.1 Introduction to Forecasting.
13.2 Time-Series Data and Components of Time-Series Data.
13.3 Forecasting Techniques and Forecasting Accuracy.
13.4 Moving Average Method.
13.5 Single Exponential Smoothing (ES).
13.6 Double Exponential Smoothing – Holt’s Method.
13.7 Triple Exponential Smoothing (Holt-Winter Model).
13.8 Croston’s Forecasting Method for Intermittent Demand.
13.9 Regression Model for Forecasting.
13.10 Auto-Regressive (AR), Moving Average (MA) and ARMA Models.
13.11 Auto-Regressive (AR) Models.
13.12 Moving Average Process MA(q).
13.13 Auto-Regressive Moving Average (ARMA) Process.
13.14 Auto-Regressive Integrated Moving Average (ARIMA) Process.
13.15 Power of Forecasting Model: Theil’s Coefficient.
14. Clustering.
14.1 Introduction to Clustering.
14.2 Distance and Dissimilarity Measures used in Clustering.
14.3 Quality and Optimal Number of Clusters.
14.4 Clustering Algorithms.
14.5 K-Means Clustering.
14.6 Hierarchical Clustering.
15. Prescriptive Analytics.
15.1 Introduction to Prescriptive Analytics.
15.2 Linear Programming.
15.3 Linear Programming (LP) Model Building.
15.4 Linear Programming Problem (LPP) Terminologies.
15.5 Assumptions of Linear Pro.gramming
15.6 Sensitivity Analysis in LPP.
15.7 Solving a Linear Programming Problem using Graphical Method.
15.8 Range of Optimality.
15.9 Range of Shadow Price.
15.10 Dual Linear Programming.
15.11 Primal−Dual Relationships.
15.12 Multi-Period (Stage) Models.
15.13 Linear Integer Programming (ILP).
15.14 Multi-Criteria Decision-Making (MCDM) Problems.
16. Stochastic Models.
16.1 Introduction Stochastic Process.
16.2 Poisson Process.
16.3 Compound Poisson Process.
16.4 Markov Chains.
16.5 Classification of States in a Markov Chain.
16.6 Markov Chains with Absorbing States.
16.7 Expected Duration to Reach a State from other States.
16.8 Calculation of Retention Probability and Customer Lifetime Value using Markov Chains.
16.9 Markov Decision Process (MDP).
16.10 Value Iteration Algorithm.
17. Six Sigma.
17.1 Introduction to Six Sigma.
17.2 What is Six Sigma?
17.3 Origins of Six Sigma.
17.4 Three-Sigma versus Six-Sigma Process.
17.5 Cost of Poor Quality.
17.6 Sigma Score.
17.7 Industrial Applications of Six Sigma.
17.8 Six Sigma Measures.
17.9 Defects Per Million Opportunities (DPMO).
17.10 Yield.
17.11 Sigma Score (or Sigma Quality Level).
17.12 DMAIC Methodology.
17.13 Six Sigma Project Selection For DMAIC Implementation.
17.14 DMAIC Methodology – Case of Armoured Vehicle.
17.15 Six Sigma Toolbox.
Summary.
Multiple Choice Questions.
Exercises.
Case Study: Era of Quality at the Akshaya Patra Foundation.
References.
Appendix.
Bibliography.
Index.

The book has 17 chapters and addresses all components of analytics such as descriptive, predictive and prescriptive analytics. The first few chapters are dedicated to foundations of business analytics. Introduction to business analytics and its components such as descriptive, predictive and prescriptive analytics along with several applications are discussed in Chapter 1. In Chapters 2 to 8, we discuss basic statistical concepts such as descriptive statistics, concept of random variables, discrete and continuous random variables, confidence interval, hypothesis testing, analysis of variance and correlation. Chapters 9 to 13 are dedicated to predictive analytics techniques such as multiple linear regression, logistic regression, decision tree learning and forecasting techniques. Clustering is discussed in Chapter 14. Chapter 15 is dedicated to prescriptive analytics in which concepts such as linear programming, integer programming, and goal programming are discussed. Stochastic models and Six Sigma are discussed in Chapters 16 and 17, respectively.