Business Analytics, Volume II: A Data Driven Decision Making Approach for Business

Description

This business analytics (BA) text discusses the models based on fact-based data to measure past business performance to guide an organization in visualizing and predicting future business performance and outcomes. It provides a comprehensive overview of analytics in general with an emphasis on predictive analytics. Given the booming interest in analytics and data science, this book is timely and informative. It brings many terms, tools, and methods of analytics together. The first three chapters provide an introduction to BA, importance of analytics, types of BA-descriptive, predictive, and prescriptive-along with the tools and models. Business intelligence (BI) and a case on descriptive analytics are discussed. Additionally, the book discusses on the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics-machine learning, neural networks, and artificial intelligence. The concluding chapter discusses on the current state, job outlook, and certifications in analytics. Cover Business Analytics: A Data-Driven Decision-Making Approach for Business Volume II Contents Preface Acknowledgments Chapter 1: Business Analytics at a Glance Introduction to Business Analytics—What Is It? Analytics and Business Analytics Business Analytics and Its Importance in Modern Business Decision Types of Business Analytics Tools of Business Analytics Most Widely Used Predictive Analytics Models Background and Prerequisites to Predictive Analytics Tools Other Areas Associated with Predictive Analytics Recent Applications and Tools of Predictive Modeling Prescriptive Analytics and Tools of Prescriptive Analytics Types of Models Glossary of Terms Related to Analytics Chapter 2: Business Analytics and Business Intelligence Business Analytics and Business Intelligence—Overview Types of Business Analytics and Their Objectives Input to Business Analytics, Types of Business Analytics, and Their Purpose Tools of Each Type of Analytics and Their Objectives Business Intelligence and Business Analytics: Differences Business Intelligence and Business Analytics: A Comparison Summary Chapter 3: Analytics, Business Analytics, Data Analytics, and How They Fit into the Broad Umbrella of Business Intelligence Introduction: Analytics, Business Analytics, and Data Analytics Business Intelligence—Defined Origin of Business Intelligence How Does Business Intelligence Fit into Overall Analytics? Business Intelligence and Support Systems Applications of Business Intelligence Tools of Business Intelligence Business Intelligence Functions and Applications Explained More Applications Areas of Analytics Purpose of Analytics Analytics as Applied to Different Areas Advanced Analytics Business Intelligence Programs in Companies Specific Areas of Business Intelligence Applications in an Enterprise Success Factors for Business Intelligence Implementation Comparing Business Intelligence with Business Analytics Where Does the Business Analytics Fit in the Scope of Business Intelligence? Difference between Business Analytics and Business Intelligence Summary Glossary of Terms Related to Business Intelligence Chapter 4: Descriptive Analytics—Overview, Applications, and a Case Overview: Descriptive Analytics Descriptive Analytics Applications: A Business Analytics Case Case Study: Buying Pattern of Online Customers in a Large Department Store Summary Chapter 5: Descriptive versus Predictive Analytics What Is Predictive Analytics and How Is It Different from Descriptive Analytics? Exploring the Relationships between the Variables—Qualitative Tools An Example of Logic-Driven Model—Cause-and-Effect Diagram Data-Driven Predictive Models and Their Applications—Quantitative Models Prerequisites and Background for Predictive Analytics Summary Appendix A–D Chapter 6: Key Predictive Analytics Models (Predicting Future Business Outcomes Using Analytic Models) Key Predictive Analytics Models and Their Description and Applications Summary Chapter 7: Regression Analysis and Modeling Introduction to Regression and Correlation Linear Regression The Estimated Equation of Regression Line The Method of Least Squares Illustration of Least Squares Regression Method Analysis of a Simple Regression Problem Constructing a Scatterplot of the Data Finding the Equation of the Best Fitting Line (Estimated Line) Interpretation of the Fitted Regression Line Making Predictions Using the Regression Line The Standard Error of the Estimate(s) Assessing the Fit of the Simple Regression Model: The Coefficient of Determination (r2) and Its Meaning The Coefficient of Correlation (r) and Its Meaning Summary of the Main Features of the Simple Regression Model Discussed Above Regression Analysis Using Computer The Coefficient of Determination (r2) Using EXCEL Multiple Regression: Computer Analysis and Results The Least Squares Multiple Regression Model Models with Two Quantitative Independent Variables x1 and x2 Assumptions of Multiple Regression Model Computer Analysis of Multiple Regression Constructing Scatter Plots and Matrix Plots Matrix of Plots: Simple Multiple Linear Regression Model The Regression Equation Interpreting the Regression Equation Standard Error of the Estimate(s) and Its Meaning The Coefficient of Multiple Determination (r2) Test the Overall Significance of Regression for the Example Problem at a 5 Percent Level of Significance Test the Hypothesis That Each of the Three Independent Variables Is Significant at a 5 Percent Level of Significance Alternate Way of Testing the above Hypothesis Multicollinearity and Autocorrelation in Multiple Regression Effects of Multicollinearity Detecting Multicollinearity Summary of the Key Features of Multiple Regression Model Model Building and Computer Analysis Another Example: Quadratic (Second-Order) Model Summary of Model Building Models with Qualitative Independent (Dummy) Variables One Qualitative Independent Variable at Two Levels Model with One Qualitative Independent Variable at Three Levels Example: Dummy Variables Overview of Regression Models Implementation Steps and Strategy for Regression Models Chapter 8: Time Series Analysis and Forecasting Introduction to Forecasting Forecasting Methods: An Overview Time Series Forecasting Associative Forecasting Some Common Patterns in Forecasting Measuring Forecast Accuracy Forecasting Methods Forecasting Models Based on Averages Simple Moving Average Weighted Moving Averages Simple Exponential Smoothing Method Example of Moving Average with a Trend or Double Moving Average Forecasting Data Using Different Methods and Comparing Forecasts to Select the Best Forecasting Method Summary Chapter 9: Data Mining: Tools and Applications in Predictive Analytics Introduction to Data Mining Summary Chapter 10: Wrap-Up, Overview, Notes on Implementation, and Current State of Business Analytics Overview Business Intelligence Statistical Analysis Data Analytics Types of Data Analytics Applications Artificial Intelligence, Machine Learning, and Deep Learning Machine Learning Deep Learning Background and Prerequisites to Predictive Analytics Future of Data Analytics and Business Analytics Certification and Online Courses in Business Analytics Summary Appendices: Background and Prerequisite for Predictive Analytics Appendix A: Probability Concepts: Role of Probability in Decision Making Appendix B: Sampling, Sampling Distribution, and Inference Procedure Appendix C: Review of Estimation, Confidence Intervals, and Hypothesis Testing Appendix D: Hypothesis Testing for One and Two Population Parameters Additional Readings About the author Index Ad Page Back Cover