7 Exploratory Data Analysis Free Online FlashCards

What is the purpose of exploratory data analysis?

Exploratory data analysis uses summaries and visualizations to examine data, reveal structure and patterns, and develop questions before formal modeling or conclusions.

What patterns can a scatter plot reveal between two numerical variables?

A scatter plot can reveal positive or negative trends, curved patterns, changing spread, clusters, and isolated points.

Which plots help compare a numerical variable across categories?

Side-by-side box plots or dot plots help compare typical values and variation across categories.

What can a time-series plot reveal?

A time-series plot can expose trends, cycles, abrupt shifts, and gaps in values over time.

What is a scatterplot matrix useful for?

A scatterplot matrix provides a quick view of pairwise patterns among several numerical variables.

Does an association between two variables prove that one causes the other?

Association alone does not establish causation; other factors may help explain the observed pattern.

Which numerical summaries can help compare groups?

Comparing group medians and ranges can support what plots suggest about typical values and variation.

Why can correlation be misleading for a curved relationship?

A correlation may fail to describe a curved relationship, so one summary statistic can miss important structure.

What is an anomaly in a dataset?

An anomaly is an observation or pattern that differs noticeably from the rest of the data.

How should you initially treat a flagged unusual observation?

Treat a flagged observation as something to investigate, not as an error to remove automatically.

What should you check when investigating an unusual value?

Check the original record, units, data-entry process, and relevant context to assess an unusual value.

What might an unusual observation indicate?

An unusual value may be an error, a rare but valid event, or evidence of distinct groups or a different pattern.