1. Chapter 16 Exploring, Displaying, and Examining Data McGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.

2. 16-2 Learning Objectives Understand... That exploratory data analysis techniques provide insights and data diagnostics by emphasizing visual representations of the data. How cross-tabulation is used to examine relationships involving categorical variables, serves as a framework for later statistical testing, and makes an efficient tool for data visualization and later decision-making.

3. 16-3 Research as Competitive Advantage “As data availability continues to increase, the importance of identifying/filtering and analyzing relevant data can be a powerful way to gain an information advantage over our competition.” Tom H.C. Anderson founder & managing partner Anderson Analytics, LLC

4. 16-4 PulsePoint: Research Revelation 65 The percent boost in company revenue created by best practices in data quality.

5. 16-5 Researcher Skill Improves Data Discovery DDW is a global player in research services. As this ad proclaims, you can “push data into a template and get the job done,” but you are unlikely to make discoveries using a template process.

6. 16-6 Exploratory Data Analysis ConfirmatoryExploratory

7. 16-7 Data Exploration, Examination, and Analysis in the Research Process

8. 16-8 Research Values the Unexpected “It is precisely because the unexpected jolts us out of our preconceived notions, our assumptions, our certainties, that it is such a fertile source of innovation.” Peter Drucker, author Innovation and Entrepreneurship

9. 16-9 Frequency of Ad Recall Value Label Value Frequency Percent Valid Cumulative Percent Percent

10. 16-10 Bar Chart

11. 16-11 Pie Chart

12. 16-12 Frequency Table

13. 16-13 Histogram

14. 16-14 Stem-and-Leaf Display 455666788889 12466799 02235678 02268 24 018 3 1 06 3 36 3 6 8 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

15. 16-15 Pareto Diagram

16. 16-16 Boxplot Components

17. 16-17 Diagnostics with Boxplots

18. 16-18 Boxplot Comparison

19. 16-19 Mapping

20. 16-20 Geograph: Digital Camera Ownership

21. 16-21 SPSS Cross-Tabulation

22. 16-22 Percentages in Cross-Tabulation

23. 16-23 Guidelines for Using Percentages Averaging percentages Use of too large percentages Using too small a base Percentage decreases can never exceed 100%

24. 16-24 Cross-Tabulation with Control and Nested Variables

25. 16-25 Automatic Interaction Detection (AID)

26. 16-26 Exploratory Data Analysis This Booth Research Services ad suggests that the researcher’s role is to make sense of data displays. Great data exploration and analysis delivers insight from data.

27. 16-27 Key Terms Automatic interaction detection (AID) Boxplot Cell Confirmatory data analysis Contingency table Control variable Cross-tabulation Exploratory data analysis (EDA) Five-number summary Frequency table Histogram Interquartile range (IQR) Marginals Nonresistant statistics Outliers Pareto diagram Resistant statistics Stem-and-leaf display

28. Working with Data Tables McGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.

29. 16-29 Original Data Table Our grateful appreciation to eMarketer for the use of their table.

30. 16-30 Arranged by Spending

31. 16-31 Arranged by No. of Purchases

32. 16-32 Arranged by Avg. Transaction, Highest

33. 16-33 Arranged by Avg. Transaction, Lowest