Business Analytics: The Science of Data-Driven Decision Making

U Dinesh Kumar

ISBN: 9788126568772

736 pages

eBook also available for institutional users 

INR 839


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.

Data Files





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 Programming

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



Multiple Choice Questions


Case Study: Era of Quality at the Akshaya Patra Foundation







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