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
- Title Page
- Copyright
- Preface
- About the Author
- Acknowledgments
- Chapter 1: Introduction to the Central Textbook Example
-
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
- Chapter 4: Data Collection & Capture
- Chapter 5: Introduction to SAS®
-
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
- Chapter 9: Using Basic Statistics to Check & Fix Data
- Chapter 10: Introduction to Graphing in SAS
- Chapter 11: The Statistics Process: Fitting Models to Data
- Chapter 12: Key Concepts: Size & Accuracy
-
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
- Chapter 16: Reporting Business Analytics
-
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
- Chapter 19: Bibliography
- Index
- Additional Resources
Product information
- Title: Business Statistics Made Easy in SAS
- Author(s):
- Release date: October 2015
- Publisher(s): SAS Institute
- ISBN: 9781629600444
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