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Practical Business Analytics Using SAS: A Hands-on Guide

Book Description

Practical Business Analytics Using SAS: A Hands-on Guide shows SAS users and businesspeople how to analyze data effectively in real-life business scenarios.

The book begins with an introduction to analytics, analytical tools, and SAS programming. The authors—both SAS, statistics, analytics, and big data experts—first show how SAS is used in business, and then how to get started programming in SAS by importing data and learning how to manipulate it. Besides illustrating SAS basic functions, you will see how each function can be used to get the information you need to improve business performance. Each chapter offers hands-on exercises drawn from real business situations.

The book then provides an overview of statistics, as well as instruction on exploring data, preparing it for analysis, and testing hypotheses. You will learn how to use SAS to perform analytics and model using both basic and advanced techniques like multiple regression, logistic regression, and time series analysis, among other topics. The book concludes with a chapter on analyzing big data. Illustrations from banking and other industries make the principles and methods come to life.

Readers will find just enough theory to understand the practical examples and case studies, which cover all industries. Written for a corporate IT and programming audience that wants to upgrade skills or enter the analytics field, this book includes:

  • More than 200 examples and exercises, including code and datasets for practice.
  • Relevant examples for all industries.
  • Case studies that show how to use SAS analytics to identify opportunities, solve complicated problems, and chart a course.
  • Practical Business Analytics Using SAS: A Hands-on Guide gives you the tools you need to gain insight into the data at your fingertips, predict business conditions for better planning, and make excellent decisions. Whether you are in retail, finance, healthcare, manufacturing, government, or any other industry, this book will help your organization increase revenue, drive down costs, improve marketing, and satisfy customers better than ever before.

    Table of Contents

    1. Cover
    2. Title
    3. Copyright
    4. Dedication
    5. Contents at a Glance
    6. Contents
    7. About the Authors
    8. Acknowledgments
    9. Preface
    10. Part 1: Basics of SAS Programming for Analytics
      1. Chapter 1: Introduction to Business Analytics and Data Analysis Tools
        1. Business Analytics, the Science of Data-Driven Decision Making
          1. Business Analytics Defined
          2. Is Advanced Analytics the Solution for You?
          3. Simulation, Modeling, and Optimization
          4. Data Warehousing and Data Mining
          5. What Can Be Discovered Using Data Mining?
          6. Business Intelligence, Reporting, and Business Analytics
        2. Analytics Techniques Used in the Industry
          1. Regression Modeling and Analysis
          2. Time Series Forecasting
          3. Conjoint Analysis
          4. Cluster Analysis
          5. Segmentation
          6. Principal Components and Factor Analysis
          7. Correspondence Analysis
          8. Survival Analytics
        3. Some Practical Applications of Business Analytics
          1. Customer Analytics
          2. Operational Analytics
          3. Social Media Analytics
          4. Data Used in Analytics
        4. Big Data vs. Conventional Business Analytics
          1. Introduction to Big Data
          2. Introduction to Data Analysis Tools
          3. Main Parts of SAS, SPSS, and R
          4. Selection of Analytics Tools
        5. The Background Required for a Successful Career in Business Analytics
          1. Skills Required for a Business Analytics Professional
        6. Conclusion
      2. Chapter 2: SAS Introduction
        1. Starting SAS in Windows
        2. The SAS Opening Screen
        3. The Five Main Windows
          1. Editor Window
          2. Log Window
          3. Output Window
          4. Explorer Window
          5. Results Window
        4. Important Menu Options and Icons
          1. View Options
          2. Run Menu
          3. Solutions Menu
          4. Shortcut Icons
        5. Writing and Executing a SAS Program
          1. Comments in the Code
        6. Your First SAS Program
        7. Debugging SAS Code Using a Log File
          1. Example for Warnings in Log File
        8. Tips for Writing, Reading the Log File, and Debugging
        9. Saving SAS Files
          1. Exercise
        10. Conclusion
      3. Chapter 3: Data Handling Using SAS
        1. SAS Data Sets
          1. Descriptive Portion of SAS Data Sets
          2. Data Portion of Data Set
        2. SAS Libraries
          1. Creating the Library Using the GUI
          2. Rules of Assigning a Library
          3. Creating a New Library Using SAS Code
          4. Permanent and Temporary Libraries
        3. Two Main Types of SAS Statements
        4. Importing Data into SAS
          1. Data Set Creation Using the SAS Program
          2. Using the Import Wizard
          3. Import Using the Code
        5. Data Manipulations
          1. Making a Copy of a SAS Data Set
          2. Creating New Variables
          3. Updating the Same Data Set
          4. Drop and Keep Variables
          5. Subsetting the Data
        6. Conclusion
      4. Chapter 4: Important SAS Functions and Procs
        1. SAS Functions
          1. Numeric Functions
          2. Character Functions
          3. Date Functions
        2. Important SAS PROCs
          1. The Proc Step
          2. PROC CONTENTS
          3. PROC SORT
        3. Graphs Using SAS
          1. PROC gplot and Gchart
          2. PROC SQL
        4. Data Merging
          1. Appending the Data
          2. From SET to MERGE
          3. Blending with Condition
          4. Matched Merging
        5. Conclusion
    11. Part 2: Using SAS for Business Analytics
      1. Chapter 5: Introduction to Statistical Analysis
        1. What Is Statistics?
        2. Basic Statistical Concepts in Business Analytics
          1. Population
          2. Sample
          3. Variable
          4. Variable Types in Predictive Modeling Context
          5. Parameter
          6. Statistic
          7. Example Exercise
        3. Statistical Analysis Methods
          1. Descriptive Statistics
          2. Inferential Statistics
          3. Predictive Statistics
        4. Solving a Problem Using Statistical Analysis
          1. Setting Up Business Objective and Planning
          2. The Data Preparation
          3. Descriptive Analysis and Visualization
          4. Predictive Modeling
          5. Model Validation
          6. Model Implementation
        5. An Example from the Real World: Credit Risk Life Cycle
          1. Business Objective and Planning
          2. Data Preparation
          3. Descriptive Analysis and Visualization
          4. Predictive Modeling
          5. Model Validation
          6. Model Implementation
        6. Conclusion
      2. Chapter 6: Basic Descriptive Statistics and Reporting in SAS
        1. Rudimentary Forms of Data Analysis
          1. Simply Print the Data
          2. Print and Various Options of Print in SAS
        2. Summary Statistics
          1. Central Tendencies
          2. Calculating Central Tendencies in SAS
          3. What Is Dispersion?
          4. Calculating Dispersion Using SAS
          5. Quantiles
          6. Calculating Quantiles Using SAS
          7. Box Plots
          8. Creating Boxplots Using SAS
        3. Bivariate Analysis
        4. Conclusion
      3. Chapter 7: Data Exploration, Validation, and Data Sanitization
        1. Data Exploration Steps in a Statistical Data Analysis Life Cycle
          1. Example: Contact Center Call Volumes
        2. Need for Data Exploration and Validation
        3. Issues with the Real-World Data and How to Solve Them
          1. Missing Values
          2. The Outliers
          3. Manual Inspection of the Dataset Is Not a Practical Solution
          4. Removing Records Is Not Always the Right Way
        4. Understanding and Preparing the Data
          1. Data Exploration
          2. Data Validation
          3. Data Cleaning
        5. Data Exploration, Validation, and Sanitization Case Study: Credit Risk Data
          1. Importing the Data
          2. Step 1: Data Exploration and Validation Using the PROC CONTENTS
          3. Step 2: Data Exploration and Validation Using Data Snapshot
          4. Step 3: Data Exploration and Validation Using Univariate Analysis
          5. Step 4: Data Exploration and Validation Using Frequencies
          6. Step 5: The Missing Value and Outlier Treatment
        6. Conclusion
      4. Chapter 8: Testing of Hypothesis
        1. Testing: An Analogy from Everyday Life
        2. What Is the Process of Testing a Hypothesis?
          1. State the Null Hypothesis on the Population: Null Hypothesis (H0)
          2. Alternate Hypothesis (H1)
          3. Sampling Distribution
          4. Central Limit Theorem
          5. Test Statistic
          6. Inference
          7. Critical Values and Critical Region
          8. Confidence Interval
        3. Tests
          1. T-test for Mean
          2. Case Study: Testing for the Mean in SAS
          3. Other Test Examples
          4. Two-Tailed and Single-Tailed Tests
        4. Conclusion
      5. Chapter 9: Correlation and Linear Regression
        1. What Is Correlation?
          1. Pearson’s Correlation Coefficient (r)
          2. Variance and Covariance
          3. Correlation Matrix
          4. Calculating Correlation Coefficient Using SAS
          5. Correlation Limits and Strength of Association
          6. Properties and Limitations of Correlation Coefficient (r)
          7. Some Examples on Limitations of Correlation
          8. Correlation vs. Causation
          9. Correlation Example
          10. Correlation Summary
        2. Linear Regression
          1. Correlation to Regression
          2. Estimation Example
        3. Simple Linear Regression
          1. Regression Line Fitting Using Least Squares
          2. The Beta Coefficients: Example 1
          3. How Good Is My Model?
          4. Regression Assumptions
        4. When Linear Regression Can’t Be Applied
        5. Simple Regression: Example
        6. Conclusion
      6. Chapter 10: Multiple Regression Analysis
        1. Multiple Linear Regression
          1. Multiple Regression Line
          2. Multiple Regression Line Fitting Using Least Squares
          3. Multiple Linear Regression in SAS
          4. Example: Smartphone Sales Estimation
          5. Goodness of Fit
          6. Three Main Measures from Regression Output
          7. Multicollinearity Defined
        2. How to Analyze the Output: Linear Regression Final Check List
          1. Double-Check for the Assumptions of Linear Regression
          2. F-test
          3. R-squared
          4. Adjusted R-Squared
          5. VIF
          6. T-test for Each Variable
          7. Analyzing the Regression Output: Final Check List Example
        3. Conclusion
      7. Chapter 11: Logistic Regression
        1. Predicting Ice-Cream Sales: Example
        2. Nonlinear Regression
        3. Logistic Regression
        4. Logistic Regression Using SAS
        5. SAS Logistic Regression Output Explanation
          1. Output Part 1: Response Variable Summary
          2. Output Part 2: Model Fit Summary
          3. Output Part 3: Test for Regression Coefficients
          4. Output Part 4: The Beta Coefficients and Odds Ratio
          5. Output Part 5: Validation Statistics
        6. Individual Impact of Independent Variables
        7. Goodness of Fit for Logistic Regression
          1. Chi-square Test
          2. Concordance
        8. Prediction Using Logistic Regression
        9. Multicollinearity in Logistic Regression
          1. No VIF Option in PROC LOGISTIC
        10. Logistic Regression Final Check List
        11. Loan Default Prediction Case Study
          1. Background and Problem Statement
          2. Objective
          3. Data Set
          4. Model Building
          5. Final Model Equation and Prediction Using the Model
        12. Conclusion
      8. Chapter 12: Time-Series Analysis and Forecasting
        1. What Is a Time-Series Process?
        2. Main Phases of Time-Series Analysis
        3. Modeling Methodologies
        4. Box–Jenkins Approach
          1. What Is ARIMA?
          2. The AR Process
          3. The MA Process
          4. ARMA Process
        5. Understanding ARIMA Using an Eyesight Measurement Analogy
        6. Steps in the Box–Jenkins Approach
          1. Step 1: Testing Whether the Time Series Is Stationary
          2. Step 2: Identifying the Model
          3. Step 3: Estimating the Parameters
          4. Step 4: Forecasting Using the Model
          5. Case Study: Time-Series Forecasting Using the SAS Example
          6. Checking the Model Accuracy
        7. Conclusion
      9. Chapter 13: Introducing Big Data Analytics
        1. Traditional Data-Handling Tools
          1. Walmart Customer Data
          2. Facebook Data
          3. Examples of the Growing Size of Data
        2. What Is Big Data?
          1. The Three Main Components of Big Data
          2. Applications of Big Data Analytics
        3. The Solution for Big Data Problems
        4. Distributed Computing
        5. What Is MapReduce?
          1. Map Function
          2. Reduce Function
        6. What Is Apache Hadoop?
          1. Hadoop Distributed File System
          2. MapReduce
          3. Apache Hive
          4. Apache Pig
          5. Other Tools in the Hadoop Ecosystem
          6. CompaniesThat Use Hadoop
        7. Big Data Analytics Example
          1. Examining the Business Problem
          2. Getting the Data Set
          3. Starting Hadoop
          4. Looking at the Hadoop Components
          5. Moving Data from the Local System to Hadoop
          6. Viewing the Data on HDFS
          7. Starting Hive
          8. Creating a Table Using Hive
          9. Executing a Program Using Hive
          10. Viewing the MapReduce Status
          11. The Final Result
        8. Conclusion
    12. Index