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Business Statistics Made Easy in SAS

Book Description

Learn or refresh core statistical methods for business with SAS® and approach real business analytics issues and techniques using a practical approach that avoids complex mathematics and instead employs easy-to-follow explanations. Business Statistics Made Easy in SAS® is designed as a user-friendly, practice-oriented, introductory text to teach businesspeople, students, and others core statistical concepts and applications. It begins with absolute core principles and takes you through an overview of statistics, data and data collection, an introduction to SAS®, and basic statistics (descriptive statistics and basic associational statistics). The book also provides an overview of statistical modeling, effect size, statistical significance and power testing, basics of linear regression, introduction to comparison of means, basics of chi-square tests for categories, extrapolating statistics to business outcomes, and some topical issues in statistics, such as big data, simulation, machine learning, and data warehousing. The book steers away from complex mathematical-based explanations, and it also avoids basing explanations on the traditional build-up of distributions, probability theory and the like, which tend to lose the practice-oriented reader. Instead, it teaches the core ideas of statistics through methods such as careful, intuitive written explanations, easy-to-follow diagrams, step-by-step technique implementation, and interesting metaphors. With no previous SAS experience necessary, Business Statistics Made Easy in SAS® is an ideal introduction for beginners. It is suitable for introductory undergraduate classes, postgraduate courses such as MBA refresher classes, and for the business practitioner. It is compatible with SAS® University Edition.

Table of Contents

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