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E-raamat: Practical Business Analytics Using SAS: A Hands-on Guide

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 07-Feb-2015
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484200438
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Practical Business Analytics Using SAS: A Hands-on GuideSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 07-Feb-2015
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484200438
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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.

About the Authors xix
Acknowledgments xxi
Preface xxiii
Part 1: Basics of SAS Programming for Analytics 1(144)
Chapter 1 Introduction to Business Analytics and Data Analysis Tools
3(26)
Business Analytics, the Science of Data-Driven Decision Making
3(5)
Business Analytics Defined
3(2)
Is Advanced Analytics the Solution for You?
5(1)
Simulation, Modeling, and Optimization
6(1)
Data Warehousing and Data Mining
7(1)
What Can Be Discovered Using Data Mining?
7(1)
Business Intelligence, Reporting, and Business Analytics
8(1)
Analytics Techniques Used in the Industry
8(6)
Regression Modeling and Analysis
8(2)
Time Series Forecasting
10(1)
Conjoint Analysis
11(1)
Cluster Analysis
11(2)
Segmentation
13(1)
Principal Components and Factor Analysis
13(1)
Correspondence Analysis
13(1)
Survival Analytics
13(1)
Some Practical Applications of Business Analytics
14(1)
Customer Analytics
14(1)
Operational Analytics
14(1)
Social Media Analytics
14(1)
Data Used in Analytics
15(1)
Big Data vs. Conventional Business Analytics
15(12)
Introduction to Big Data
15(5)
Introduction to Data Analysis Tools
20(2)
Main Parts of SAS, SPSS, and R
22(5)
Selection of Analytics Tools
27(1)
The Background Required for a Successful Career in Business Analytics
27(1)
Skills Required for a Business Analytics Professional
27(1)
Conclusion
28(1)
Chapter 2 SAS Introduction
29(26)
Starting SAS in Windows
29(2)
The SAS Opening Screen
31(1)
The Five Main Windows
31(11)
Editor Window
32(2)
Log Window
34(1)
Output Window
35(5)
Explorer Window
40(1)
Results Window
41(1)
Important Menu Options and Icons
42(4)
View Options
44(1)
Run Menu
44(1)
Solutions Menu
45(1)
Shortcut Icons
45(1)
Writing and Executing a SAS Program
46(9)
Comments in the Code
47(1)
Your First SAS Program
48(2)
Debugging SAS Code Using a Log File
50(2)
Example for Warnings in Log File
52(1)
Tips for Writing, Reading the Log File, and Debugging
53(1)
Saving SAS Files
53(1)
Exercise
54(1)
Conclusion
54(1)
Chapter 3 Data Handling Using SAS
55(40)
SAS Data Sets
56(2)
Descriptive Portion of SAS Data Sets
56(1)
Data Portion of Data Set
57(1)
SAS Libraries
58(10)
Creating the Library Using the GUI
59(5)
Rules of Assigning a Library
64(1)
Creating a New Library Using SAS Code
64(1)
Permanent and Temporary Libraries
65(3)
Two Main Types of SAS Statements
68(1)
Importing Data into SAS
68(12)
Data Set Creation Using the SAS Program
68(2)
Using the Import Wizard
70(7)
Import Using the Code
77(3)
Data Manipulations
80(13)
Making a Copy of a SAS Data Set
80(2)
Creating New Variables
82(5)
Updating the Same Data Set
87(1)
Drop and Keep Variables
88(2)
Subsetting the Data
90(3)
Conclusion
93(2)
Chapter 4 Important SAS Functions and Procs
95(50)
SAS Functions
95(13)
Numeric Functions
96(5)
Character Functions
101(4)
Date Functions
105(3)
Important SAS PROCs
108(12)
The Proc Step
108(1)
PROC CONTENTS
108(4)
PROC SORT
112(8)
Graphs Using SAS
120(9)
PROC gplot and Gchart
121(4)
PROC SQL
125(4)
Data Merging
129(14)
Appending the Data
129(2)
From SET to MERGE
131(1)
Blending with Condition
132(2)
Matched Merging
134(9)
Conclusion
143(2)
Part 2: Using SAS for Business Analytics 145(396)
Chapter 5 Introduction to Statistical Analysis
147(18)
What Is Statistics?
147(2)
Basic Statistical Concepts in Business Analytics
149(11)
Population
149(1)
Sample
149(1)
Variable
150(1)
Variable Types in Predictive Modeling Context
151(1)
Parameter
151(1)
Statistic
152(1)
Example Exercise
152(8)
Statistical Analysis Methods
160(1)
Descriptive Statistics
160(1)
Inferential Statistics
160(1)
Predictive Statistics
161(1)
Solving a Problem Using Statistical Analysis
161(2)
Setting Up Business Objective and Planning
161(1)
The Data Preparation
161(1)
Descriptive Analysis and Visualization
161(1)
Predictive Modeling
162(1)
Model Validation
162(1)
Model Implementation
162(1)
An Example from the Real World: Credit Risk Life Cycle
163(1)
Business Objective and Planning
163(1)
Data Preparation
163(1)
Descriptive Analysis and Visualization
163(1)
Predictive Modeling
164(1)
Model Validation
164(1)
Model Implementation
164(1)
Conclusion
164(1)
Chapter 6 Basic Descriptive Statistics and Reporting in SAS
165(32)
Rudimentary Forms of Data Analysis
165(3)
Simply Print the Data
165(1)
Print and Various Options of Print in SAS
165(3)
Summary Statistics
168(28)
Central Tendencies
169(4)
Calculating Central Tendencies in SAS
173(4)
What Is Dispersion?
177(5)
Calculating Dispersion Using SAS
182(3)
Quantiles
185(2)
Calculating Quantiles Using SAS
187(2)
Box Plots
189(3)
Creating Boxplots Using SAS
192(4)
Bivariate Analysis
196(1)
Conclusion
196(1)
Chapter 7 Data Exploration, Validation, and Data Sanitization
197(64)
Data Exploration Steps in a Statistical Data Analysis Life Cycle
197(4)
Example: Contact Center Call Volumes
198(3)
Need for Data Exploration and Validation
201(3)
Issues with the Real-World Data and How to Solve Them
204(2)
Missing Values
204(1)
The Outliers
205(1)
Manual Inspection of the Dataset Is Not a Practical Solution
205(1)
Removing Records Is Not Always the Right Way
205(1)
Understanding and Preparing the Data
206(1)
Data Exploration
206(1)
Data Validation
206(1)
Data Cleaning
207(1)
Data Exploration, Validation, and Sanitization Case Study: Credit Risk Data
207(52)
Importing the Data
210(1)
Step 1: Data Exploration and Validation Using the PROC CONTENTS
211(3)
Step 2: Data Exploration and Validation Using Data Snapshot
214(7)
Step 3: Data Exploration and Validation Using Univariate Analysis
221(11)
Step 4: Data Exploration and Validation Using Frequencies
232(7)
Step 5: The Missing Value and Outlier Treatment
239(20)
Conclusion
259(2)
Chapter 8 Testing of Hypothesis
261(34)
Testing: An Analogy from Everyday Life
261(1)
What Is the Process of Testing a Hypothesis?
262(21)
State the Null Hypothesis on the Population: Null Hypothesis (H0)
266(1)
Alternate Hypothesis (H1)
266(1)
Sampling Distribution
267(2)
Central Limit Theorem
269(3)
Test Statistic
272(2)
Inference
274(5)
Critical Values and Critical Region
279(1)
Confidence Interval
280(3)
Tests
283(10)
T-test for Mean
283(1)
Case Study: Testing for the Mean in SAS
283(3)
Other Test Examples
286(1)
Two-Tailed and Single-Tailed Tests
287(6)
Conclusion
293(2)
Chapter 9 Correlation and Linear Regression
295(56)
What Is Correlation?
295(23)
Pearson's Correlation Coefficient (r)
297(1)
Variance and Covariance
297(1)
Correlation Matrix
298(1)
Calculating Correlation Coefficient Using SAS
298(3)
Correlation Limits and Strength of Association
301(5)
Properties and Limitations of Correlation Coefficient (r)
306(1)
Some Examples on Limitations of Correlation
306(6)
Correlation vs. Causation
312(1)
Correlation Example
313(5)
Correlation Summary
318(1)
Linear Regression
318(7)
Correlation to Regression
320(2)
Estimation Example
322(3)
Simple Linear Regression
325(19)
Regression Line Fitting Using Least Squares
325(2)
The Beta Coefficients: Example 1
327(1)
How Good Is My Model?
328(7)
Regression Assumptions
335(9)
When Linear Regression Can't Be Applied
344(1)
Simple Regression: Example
345(4)
Conclusion
349(2)
Chapter 10 Multiple Regression Analysis
351(50)
Multiple Linear Regression
351(44)
Multiple Regression Line
353(1)
Multiple Regression Line Fitting Using Least Squares
354(1)
Multiple Linear Regression in SAS
355(1)
Example: Smartphone Sales Estimation
355(2)
Goodness of Fit
357(1)
Three Main Measures from Regression Output
358(25)
Multicollinearity Defined
383(12)
How to Analyze the Output: Linear Regression Final Check List
395(4)
Double-Check for the Assumptions of Linear Regression
395(1)
F-test
395(1)
R-squared
395(1)
Adjusted R-Squared
395(1)
VIF
396(1)
T-test for Each Variable
396(1)
Analyzing the Regression Output: Final Check List Example
396(3)
Conclusion
399(2)
Chapter 11 Logistic Regression
401(40)
Predicting Ice-Cream Sales: Example
401(3)
Nonlinear Regression
404(3)
Logistic Regression
407(1)
Logistic Regression Using SAS
408(2)
SAS Logistic Regression Output Explanation
410(5)
Output Part 1: Response Variable Summary
410(2)
Output Part 2: Model Fit Summary
412(1)
Output Part 3: Test for Regression Coefficients
412(1)
Output Part 4: The Beta Coefficients and Odds Ratio
413(2)
Output Part 5: Validation Statistics
415(1)
Individual Impact of Independent Variables
415(1)
Goodness of Fit for Logistic Regression
416(3)
Chi-square Test
416(1)
Concordance
417(2)
Prediction Using Logistic Regression
419(1)
Multicollinearity in Logistic Regression
419(2)
No VIF Option in PROC LOGISTIC
421(1)
Logistic Regression Final Check List
421(1)
Loan Default Prediction Case Study
422(18)
Background and Problem Statement
422(1)
Objective
422(1)
Data Set
422(4)
Model Building
426(12)
Final Model Equation and Prediction Using the Model
438(2)
Conclusion
440(1)
Chapter 12 Time-Series Analysis and Forecasting
441(68)
What Is a lime-Series Process?
441(4)
Main Phases of Time-Series Analysis
445(1)
Modeling Methodologies
445(1)
Box-Jenkins Approach
446(6)
What Is ARIMA?
446(1)
The AR Process
446(2)
The MA Process
448(2)
ARMA Process
450(2)
Understanding ARIMA Using an Eyesight Measurement Analogy
452(1)
Steps in the Box-Jenkins Approach
453(54)
Step 1: Testing Whether the Time Series Is Stationary
454(11)
Step 2: Identifying the Model
465(32)
Step 3: Estimating the Parameters
497(4)
Step 4: Forecasting Using the Model
501(2)
Case Study: Time-Series Forecasting Using the SAS Example
503(3)
Checking the Model Accuracy
506(1)
Conclusion
507(2)
Chapter 13 Introducing Big Data Analytics
509(32)
Traditional Data-Handling Tools
509(2)
Walmart Customer Data
509(1)
Facebook Data
510(1)
Examples of the Growing Size of Data
510(1)
What Is Big Data?
511(3)
The Three Main Components of Big Data
511(2)
Applications of Big Data Analytics
513(1)
The Solution for Big Data Problems
514(1)
Distributed Computing
514(1)
What Is MapReduce?
515(2)
Map Function
515(1)
Reduce Function
515(2)
What Is Apache Hadoop?
517(7)
Hadoop Distributed File System
517(2)
MapReduce
519(1)
Apache Hive
520(1)
Apache Pig
521(1)
Other Tools in the Hadoop Ecosystem
521(2)
Companies That Use Hadoop
523(1)
Big Data Analytics Example
524(16)
Examining the Business Problem
524(1)
Getting the Data Set
525(1)
Starting Hadoop
525(2)
Looking at the Hadoop Components
527(2)
Moving Data from the Local System to Hadoop
529(1)
Viewing the Data on HDFS
530(4)
Starting Hive
534(1)
Creating a Table Using Hive
535(1)
Executing a Program Using Hive
536(1)
Viewing the MapReduce Status
537(2)
The Final Result
539(1)
Conclusion
540(1)
Index 541
Shailendra Kadre is a senior IT and management consultant from Bangalore, India. He is the author of a 2011 Apress book, Going Corporate: A Geeks Guide, which covers IT operations management and the business aspects of IT. He has more than 20 years of experience in manufacturing, IT delivery and operations, program management, pre-sales, enterprise sales, and business analytics. Currently, he is working with Hewlett-Packard India as a solutions consultant. His current interests include business analytics and enterprise printing solutions.Shailendra earned his masters degree in mechanical engineering from the Indian Institute of Technology (IIT), Delhi. He lives in Bangalore with his wife Meenakshi, daughter Neha, and son Vivek. He can be contacted at shailendrakadre@gmail.com.
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