Business analytics : a data-driven decision-making approach for business. Volume II, Predictive analytics / Amar Sahay.

Format:

Book

Author/Creator:

Sahay, Amar, author.

Language:

English

Subjects (All):

Management--Statistical methods.

Management.

Decision making--Statistical methods.

Decision making.

Physical Description:

1 online resource (405 pages)

Edition:

1st ed.

Place of Publication:

New York : Business Expert Press, 2020.

Summary:

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.

Contents:

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

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

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

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

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

Chapter 9: Data Mining: Tools and Applications in Predictive Analytics

Introduction to Data Mining

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

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.

Notes:

Includes index.

Description based on print version record.

ISBN:

9781631574801

1631574809