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Chapter 16

Chapter 16. Exploring, Displaying, and Examining Data. Learning Objectives. Understand . . . That exploratory data analysis techniques provide insights and data diagnostics by emphasizing visual representations of the data.

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Chapter 16

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  1. Chapter 16 Exploring, Displaying, and Examining Data

  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. 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 that process.

  4. Exploratory Data Analysis Exploratory Confirmatory

  5. Data Exploration, Examination, and Analysis in the Research Process

  6. Value Label Value Frequency Percent Valid Cumulative Percent Percent Frequency of Ad Recall

  7. Bar Chart

  8. Pie Chart

  9. Frequency Table

  10. Histogram

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

  12. Pareto Diagram

  13. Boxplot Components

  14. Diagnostics with Boxplots

  15. Boxplot Comparison

  16. Mapping

  17. Geograph: Digital Camera Ownership

  18. SPSS Cross-Tabulation

  19. Percentages in Cross-Tabulation

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

  21. Cross-Tabulation with Control and Nested Variables

  22. Automatic Interaction Detection (AID)

  23. 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.

  24. 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 Key Terms

  25. 1-25 Working with Data Tables

  26. Original Data Table Our grateful appreciation to eMarketer for the use of their table.

  27. Arranged by Spending

  28. Arranged by No. of Purchases

  29. Arranged by Avg. Transaction, Highest

  30. Arranged by Avg. Transaction, Lowest

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