1Chapter 1 Introduction to Business Analytics
564.2 Probability Theory – Terminology: 4.2.1 Algebra of Events
21.1 Introduction
574.3 Fundamental Concepts in Probability – Axioms of Probability
31.2 WHY ANALYTICS
584.3.1 Joint and Marginal Probability
41.3 What is Business Analysis?
594.3.2 Independent Events
51.4 Business Analytics: The Science of Data-Driven Decision Making
604.3.3 Conditional Probability
61.5 Who Performs Business Analysis?
614.4 BAYES’ THEOREM: 4.4.1 Solving Monty Hall’s Problem Using Bayes’ Theorem
71.6 The Competitive Advantage of Business Analytics
624.5 Random variables
81.6.1 ADVANTAGES OF BUSINESS ANALYTICS
634.5.1 Discrete Random Variables
91.6.2 Competitive Advantage
644.5.2 Continuous Random Variables
101.6.2.1 Maximizing Economies of Scale
654.5.3 Independent Random Variables
111.6.2.2 Maximizing Economies of Scope
664.6 Binomial distribution
121.6.2.3 Quality Improvement
674.7 Poisson distribution
131.7 CHALLENGES OF BUSINESS ANALYTICS
684.8 Geometric Distribution
141.7.1 Minimizing Transaction Costs
694.9 Normal Distribution
151.7.2 Bounded Rationality
704.10 STUDENT’S t-DISTRIBUTION
161.8 Summary
714.11 Summary
171.9 FAQ’s: 1.10 References
724.12 FAQ’s
18Chapter 2 Scope of Business Analytics
734.13 References
192.1 Introduction
74 Chapter 5 Predictive Analytics
202.2 Goals of business analytics
755.1 Introduction
212.3 Domains of business analytics
765.2 Predictive Analytics Tools
222.4 Business analytics types
775.3 Predictive Analytics Techniques
232.4.1 Descriptive analytics
785.4 Trendlines and Regression Analysis
242.4.2 PREDICTIVE ANALYTICS
795.4.1 Modeling Relationships and Trends in Data
252.4.3 Prescriptive analytics
805.4.2 Simple Linear Regression
262.5 DESCRIPTIVE, PREDICTIVE, AND PRESCRIPTIVE ANALYTICS TECHNIQUES
815.4.3 Least-Squares Regression
272.6 FAQs: 2.7 References
825.4.4 The Standard Error of the Estimate(s)
28Chapter 3 Descriptive Analytics
835.4.5 Regression Analysis Using Computer
293.1 Introduction
845.4.6 Multiple linear aggression
303.2 Basic concepts
855.5 Advantages and disadvantages of regression model
313.2.1 Populations and Samples
865.6 Summary
323.2.2 Data Sets, Variables, and Observations
875.7 FAQ’s: 4.8 References
333.3 Types of Data
88 Chapter 6 Time Series Analysis and Forecasting
343.3.1 Structured and Unstructured Data
896.1 Introduction
353.3.2 Cross-sectional, Time Series, and Panel Data
906.2 Time Series Forecasting
363.4 Types of Data Measurement Scales
916.3 Some Common Patterns in Forecasting
373.5 Measures of Central Tendency
926.4 Random Fluctuations
383.5.1 Mean
936.5 Measuring Forecast Accuracy
393.5.2 Median
946.6 Forecasting Methods
403.5.3 Mode
956.6.1 Naïve Forecasting Method
413.6 Percentile, Decile, and Quartile
966.6.2 Forecasting Models Based on Averages
423.7 Measures of Variation/Dispersion
976.7 Forecasting Data Using Different Methods and Comparing Forecasts to Select the Best Forecasting Method
433.8 Measures of Shape – Skewness, and Kurtosis
986.8 Summary
443.9 Data Visualization
996.9 FAQ’s: 6.10 References
453.9.1 Histogram
100Chapter 7 Introduction to Data Mining
463.9.2 Bar Chart
1017.1 Introduction
473.9.3 Pie Chart
1027.2 The Scope of Data Mining
483.9.4 Scatter Plot
1037.3 CORE IDEAS IN DATA MINING
493.9.5 Coxcomb Chart
1047.4 Some Application Areas of Data Mining
503.9.6 Box Plot (or Box and Whisker Plot)
1057.6 Data Mining Methodologies: Data Mining Tasks
513.9.7 Treemap
1067.6.1 Difference between Descriptive and Predictive Data Mining
523.10 Summary
1077.6.2 Additional Tools and Applications of Predictive Analytics: Data Mining Tasks
533.11 Faq’s : 3.12 References
1087.7 Summary
54Chapter 4 Introduction to Probability
1097.8 FAQs: 7.9 References
554.1 Introduction
110Index
