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020			_a9788126568772
082			_a658.5 _bKUM
100			_aKumar, U Dinesh _9836
245			_aBusiness analytics: the science of data - driven decision making
260			_bWiley India Pvt. Ltd. _aNew Delhi _c2017
300			_axxi, 714 p.
365			_aINR _b729.00
504			_aTable of Content 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 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 Summary Multiple Choice Questions Exercises Case Study: Era of Quality at the Akshaya Patra Foundation References Appendix Bibliography Index
520			_aThe 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.
650			_aMathematical statistics _9837
650			_aProgramming languages (Electronic computers) _9838
650			_aBusiness logistics _9435
650			_aData mining _9365
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