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

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

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.

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

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

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.

16-6 Exploratory Data Analysis ConfirmatoryExploratory

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

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

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

16-10 Bar Chart

16-11 Pie Chart

16-12 Frequency Table

16-13 Histogram

16-14 Stem-and-Leaf Display

16-15 Pareto Diagram

16-16 Boxplot Components

16-17 Diagnostics with Boxplots

16-18 Boxplot Comparison

16-19 Mapping

16-20 Geograph: Digital Camera Ownership

16-21 SPSS Cross-Tabulation

16-22 Percentages in Cross-Tabulation

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

16-24 Cross-Tabulation with Control and Nested Variables

16-25 Automatic Interaction Detection (AID)

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.

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

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

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

16-30 Arranged by Spending

16-31 Arranged by No. of Purchases

16-32 Arranged by Avg. Transaction, Highest

16-33 Arranged by Avg. Transaction, Lowest