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Index
Title Page
Copyright
Preface
About the Author
Acknowledgments
Chapter 1: Introduction to the Central Textbook Example
Introduction
The Company
Current Research Needs of the Company
Your Brief for the Case Example
Extended Analytical Skills Needed in the Project
Chapter 2: Introduction to the Statistics Process
Introductory Case: Big Data in the Airline Industry
Introduction to the Statistics Process
Step 1: Your Needs & Requirements
Step 2: Getting Data
Step 3: Extracting Statistics from the Data
Step 4: Understanding & Decision Making
Summary: Challenges in the Statistics Process
Advice to the Statistically Terrified
Chapter 3: Introduction to Data
Introductory Case: Royal FrieslandCampina
Brief Introduction to Samples, Populations & Data
Basic Characteristics of Variables
Chapter 4: Data Collection & Capture
Introduction
Correct Sampling
Choose Constructs and Variable Measurements
Initial Data Capture: Which Package?
Dealing with Data Once It Has Been Captured
Database & Data Analysis Software
Some Complications in Datasets
End Notes
Chapter 5: Introduction to SAS®
Introductory Vignette: SAS On Top of the Analytics World
Brief Introduction to SAS
Introduction to the Textbook Materials
Getting Started with SAS 9 or SAS Studio
End Notes
Chapter 6: Basics of SAS Programs, Data Manipulation, Analysis & Reporting
Introduction
The Running Data Example
The Pre-Analysis Data Cleaning & Preparation Steps
Overview of the Three Big Tasks in Business Statistics
Basic Introduction to SAS Programming
Major Task #1: Data Manipulation in SAS
Major Task #2: Data Analysis
Major Task #3: SAS Reporting through Output Formats
The Visual Programmer Mode in SAS Studio
Conclusion
Chapter 7: Descriptive Statistics: Understand your Data
Introductory Case: 2007 AngloGold Ashanti Look Ahead
Introduction
End Outcome of a Descriptive Statistics Analysis
Getting Descriptive Statistics in SAS
Statistics Measuring Centrality
Basic Statistics Assessing Variable Spread
Assessing Shape of a Variable’s Distribution
Conclusion on Descriptive Statistics
Appendix A to Chapter 7: Basic Normality Statistics
End Notes
Chapter 8: Basics of Associating Variables
Introduction
What is Statistical Association?
Association Does Not Mean Causation
Overview of Associations for Different Variable Types
Relating Continuous or Ordinal Data: Correlation & Covariance
Relating Categorical Variables
End Notes
Chapter 9: Using Basic Statistics to Check & Fix Data
Introduction
Inappropriate Data Points
Dealing Practically with Missing Data
Checking Centrality & Spread
Strange Variable Distributions
Dealing Practically with Multi-Item Scales
Chapter 10: Introduction to Graphing in SAS
Introduction
Major Graphing Procedures in SAS
The PROC SGPLOT Routine in SAS
Multiple Plots Simultaneously through PROC SGPANEL
Business Dashboards through PROC GKPI
Geographical Mapping Using PROC GMAP
PROC SGSCATTER for Multiple Scatterplots
Conclusion on SAS Graphing
Chapter 11: The Statistics Process: Fitting Models to Data
Introduction
Look for Patterns in the Data (Fit)
Step 3: Interpret the Pattern
Summary of the Statistics Process
End Notes
Chapter 12: Key Concepts: Size & Accuracy
Illustrative Case: Pharmaceuticals I – AstraZeneca’s Crestor
Introduction
Issue # 1: Size of a Statistic
Issue # 2: Accuracy of Statistics
The Aspects of Inaccuracy
Putting Statistical Size and Accuracy Together
Conclusion
Appendix A to Chapter 12: More on Accuracy (optional)
End Notes
Chapter 13: Introduction to Linear Regression
Illustrative Case: West Point
Introduction
The Core Textbook Case Example for Chapter 13
Introduction to Linear Regression
A Pictorial Walk through Regression
Implementing Multiple Regression in SAS
Step 1: Collect, Capture and Clean Data
Step 2: Run an Initial Regression Analysis
Step 3: Assess Fit and Apply Remedies If Necessary
Step 4: Interpret the Regression Slopes
Step 5: Reporting a Multiple Regression Result
Other Statistical Forms
Conclusion
End Notes
Chapter 14: Categories Explaining a Continuous Variable: Comparing Two Means
Introduction to Comparison of Categories
Features of the Continuous Variable to Compare Across Categories
Two Types of Categories to Compare
Numbers of Categories to Compare: Two vs. More than Two
Data Assumptions and Alternatives when Comparing Categories
Comparing Two Means: T-Tests
Comparing Means for More than Two Categories: ANOVA
End Notes
Chapter 15: Categorical Data Distributions & Associations
Introduction
Repeat: One-Way Categorical Distributions
Repeat: Linking Categorical Variables Together
Further Statistical Questions about Categorical Data
Assessing One-Way Frequencies
Tests of Categorical Variable Association
Conclusion on Categorical Data Analysis
End Notes
Chapter 16: Reporting Business Analytics
Reminder - Your Brief for the Textbook Case Study
Your Tasks in the Analytics and Reporting Stages
Background Analyses Versus Displayed Reports for the CEO
Conclusion on Business Statistics Reporting
Chapter 17: Business Analysis from Statistics: Introduction
Case Study: Oracle South Africa
Introduction
Overall Financial Extrapolation Process
Step 1: Statistics Gives Level of or Change in Focal Variables
Step 2: Financial Estimates of Revenue or Cost of One Unit
Step 3: Combine Statistics with Per-Unit Financial Values
Step 4: Include Scope
Steps 5 and 6: Net Profitability Calculations
Some Simple Examples of Business Extrapolation
Conclusion of Statistical Business Extrapolation
Chapter 18: Miscellaneous Business Statistics Topics
Introduction
Big Data
Data Warehousing
Machine Learning & Algorithms
Simulation in Business Situations
Bayesian Statistics
Conclusion
End Notes
Chapter 19: Bibliography
Books and Articles
Index
Additional Resources
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