Business Analytics, 2ed: The Science of Data-Driven Decision Making

U. Dinesh Kumar

ISBN: 9789354246197

648 pages

INR 879

Description

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.

1. 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

 

 

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