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005			20221018152212.0
008			220906b ||||| |||| 00| 0 eng d
020			_a9789354246197
082			_a658.5 _bKUM
100			_aKumar, U. Dinesh _9836
245			_aBusiness analytics: the science of data-driven decision making
250			_a2nd
260			_bWiley India Pvt. Ltd. _aNew Delhi _c2022
300			_axxii, 623 p.
365			_aINR _b909.00
504			_a1. Introduction to Business Analytics 1.1 Introduction to Business Analytics 1.2 Analytics Landscape 1.3 Why Analytics 1.4 Business Analytics: The Science of Data-Driven Decision Making 1.5 Descriptive Analytics 1.6 Predictive Analytics 1.7 Prescriptive Analytics 1.8 Descriptive, Predictive, and Prescriptive Analytics Techniques 1.9 Big Data Analytics 2. Foundations of Data Science: Descriptive Analytics 2.1 Introduction to Descriptive Analytics 2.2 Data Types and Scales of Variable Measurement 2.3 Types of Variable Measurement Scales 2.4 Population and Sample 2.5 Measures of Central Tendency 2.6 Percentile, Decile and Quartile 2.7 Measures of Variation 2.8 Measures of Shape − Skewness and Kurtosis 2.9 Data Visualization 2.10 Feature Engineering Using 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 and Cumulative Distribution Function 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 3.14 Normal 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 When Population Variance is Known: One-Sample Z-Test 6.6 Hypothesis Testing of Population Proportion: Z-Test for Proportion 6.7 Hypothesis Test for Population Mean When Population Variance is Unknown: One-Sample 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 (F Test) 6.13 Non-Parametric Tests: Chi-Square Tests 7. Analysis of Variance 7.1 Introduction to ANOVA 7.2 Multiple t-Tests for Comparing Several Means 7.3 One-Way ANOVA 7.4 Two-Way 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 SLR Model Building 9.4 Estimation of Parameters Using OLS 9.5 Interpretation of SLR Coefficients 9.6 Validation of the SLR Model 9.7 Outlier Analysis 9.8 Confidence Interval for Regression Coefficients β0 and β1 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 MLR 10.3 MLR 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 Coefficient 10.7 Regression Models with Qualitative Variables 10.8 Validation of Multiple Regression Model 10.9 Coefficient 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 an MLR Model – Partial F-Test 10.13 Residual Analysis in MLR 10.14 Multi-Collinearity and Variance Inflation Factor 10.15 Auto-Correlation 10.16 Distance Measures and Outliers Diagnostics 10.17 Feature Selection in Regression Model Building (Forward, Backward and Stepwise Regression) 10.18 Avoiding Overfitting – Mallows’s Cp 10.19 Transformations 10.20 Omitted Variable Bias 10.21 Regression Model Deployment 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 Feature (Variable) Selection in Logistic Regression 11.9 Application of Logistic Regression in Credit Scoring 11.10 Gain Chart and Lift Chart 11.11 Multinomial Logistic Regression 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 Regression Tree 12.6 Error Matrix and AUC for 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 (SES) 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 Similarity 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 and Reinforcement Learning 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) and Reinforcement Learning 16.10 Value Iteration Algorithm 17. Ensemble Methods 17.1 Ensemble Methods: Introduction 17.2 Condorcet’s Jury Theorem 17.3 Random Forest 17.4 Choice of Hyper-parameter Values in Random Forest 17.5 Random Forest Model Development 17.6 Variable Importance 17.7 Sampling Procedures to Improve Accuracy in Random Forest Model 17.8 Boosting 17.9 Gradient Boosting 18. Six Sigma 18.1 Introduction to Six Sigma 18.2 What is Six Sigma? 18.3 Origins of Six Sigma 18.4 Three-Sigma Versus Six-Sigma Process 18.5 Cost of Poor Quality 18.6 Sigma Score 18.7 Industrial Applications of Six Sigma 18.8 Six Sigma Measures 18.9 Defects Per Million Opportunities (DPMO) 18.10 Yield 18.11 Sigma Score (or Sigma Quality Level) 18.12 DMAIC Methodology 18.13 Six Sigma Project Selection for DMAIC Implementation 18.14 DMAIC Methodology – Case of Armoured Vehicle 18.15 Six Sigma Toolbox Summary Multiple Choice Questions Exercises Case Study: Era of Quality at the Akshaya Patra Foundation References Appendix Index
520			_aDescription Business Analytics has become one of the most important skills that every student of Management and Engineering should acquire to become successful in their career. The use of analytics across industries for decision making, problem solving, and driving organizational innovation makes it an essential skill to develop. Analytics is used as a competitive strategy by many successful companies.
650			_aMathematical statistics _9837
650			_aProgramming languages (Electronic computers) _9838
650			_aBusiness logistics _9435
650			_aComputer programming _99693
650			_aComputer science _91018
942			_2ddc _cBK