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Chapter 17. Exploring, Displaying, and Examining Data. Learning Objectives. Understand . . . exploratory data analysis techniques provide insights and data diagnostics by emphasizing visual representations of the data
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Chapter 17 Exploring, Displaying, and Examining Data
Learning Objectives Understand . . . • 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
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
Data Analysis Exploratory Confirmatory
Exhibit 17-1 Data Exploration, Examination, and Analysis in the Research Process
Value Label Value Frequency Percent Valid Cumulative Percent Percent Exhibit 17-2 Frequency of Ad Recall
Guidelines for Using Percentages Averaging percentages Use of too large percentages Using too small a base Percentage decreases can never exceed 100%
Exhibit 17-13 Cross-Tabulation with Control and Nested Variables
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