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:
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
- Cover
- Title
- Copyright
- Dedication
- Contents at a Glance
- Contents
- About the Authors
- Acknowledgments
- Preface
-
Part 1: Basics of SAS Programming for Analytics
- Chapter 1: Introduction to Business Analytics and Data Analysis Tools
- Chapter 2: SAS Introduction
- Chapter 3: Data Handling Using SAS
- Chapter 4: Important SAS Functions and Procs
-
Part 2: Using SAS for Business Analytics
- Chapter 5: Introduction to Statistical Analysis
- Chapter 6: Basic Descriptive Statistics and Reporting in SAS
-
Chapter 7: Data Exploration, Validation, and Data Sanitization
- Data Exploration Steps in a Statistical Data Analysis Life Cycle
- Need for Data Exploration and Validation
- Issues with the Real-World Data and How to Solve Them
- Understanding and Preparing the Data
-
Data Exploration, Validation, and Sanitization Case Study: Credit Risk Data
- Importing the Data
- Step 1: Data Exploration and Validation Using the PROC CONTENTS
- Step 2: Data Exploration and Validation Using Data Snapshot
- Step 3: Data Exploration and Validation Using Univariate Analysis
- Step 4: Data Exploration and Validation Using Frequencies
- Step 5: The Missing Value and Outlier Treatment
- Conclusion
- Chapter 8: Testing of Hypothesis
-
Chapter 9: Correlation and Linear Regression
-
What Is Correlation?
- Pearson’s Correlation Coefficient (r)
- Variance and Covariance
- Correlation Matrix
- Calculating Correlation Coefficient Using SAS
- Correlation Limits and Strength of Association
- Properties and Limitations of Correlation Coefficient (r)
- Some Examples on Limitations of Correlation
- Correlation vs. Causation
- Correlation Example
- Correlation Summary
- Linear Regression
- Simple Linear Regression
- When Linear Regression Can’t Be Applied
- Simple Regression: Example
- Conclusion
-
What Is Correlation?
- Chapter 10: Multiple Regression Analysis
-
Chapter 11: Logistic Regression
- Predicting Ice-Cream Sales: Example
- Nonlinear Regression
- Logistic Regression
- Logistic Regression Using SAS
- SAS Logistic Regression Output Explanation
- Individual Impact of Independent Variables
- Goodness of Fit for Logistic Regression
- Prediction Using Logistic Regression
- Multicollinearity in Logistic Regression
- Logistic Regression Final Check List
- Loan Default Prediction Case Study
- Conclusion
- Chapter 12: Time-Series Analysis and Forecasting
- Chapter 13: Introducing Big Data Analytics
- Index
Product information
- Title: Practical Business Analytics Using SAS: A Hands-on Guide
- Author(s):
- Release date: February 2015
- Publisher(s): Apress
- ISBN: 9781484200438
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