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Interactive·

Scatterplots & Boxplots

These plots expose relationship shape and unusual observations that summary numbers can hide.

A correlation of 0.7 does not tell you whether the points cluster around one straight line, split into two groups, or are driven by one unusual observation. Graphs reveal structures that summary statistics conceal.

A scatterplot displays each observation for two variables. A boxplot compresses one variable into its median, quartiles, whiskers, and potential outliers.

Key question

How can we read a two-variable relationship and a one-variable distribution safely from graphs?

1

Concept

What does a scatterplot show?

A scatterplot represents observations for two numeric variables as points

  • Direction: does y tend to rise or fall as x increases?
  • Form: is the pattern straight, curved, or step-like?
  • Strength: how tightly do points follow the pattern?
  • Groups: are there distinct clusters or subgroups?
  • Exceptions: are there outliers or influential observations?

Check the axes

Different ranges, units, or transformations can make the same relationship look very different.

2

Why It Matters

What does a boxplot compress?

A boxplot compresses the median, quartiles, and potential outliers

ElementMeaning
Bottom of boxQ1
Line inside boxMedian, Q2
Top of boxQ3
Box lengthIQR=Q3−Q1
Whiskersusually the farthest values within 1.5×IQR
Points beyond whiskerspotential outliers

Boxplots are useful for quickly comparing centers and spreads across groups, but they hide the exact point distribution and the number of peaks.

3

How It Works

How should outliers be handled?

A potential outlier is something to investigate, not automatically remove

If a point beyond the whiskers is a data-entry error, it should be corrected. If it is a rare but valid case, it may contain important information. Before excluding it, inspect the raw data, the measurement process, and its effect on the analysis.

Point characteristic in regressionPossible meaning
Far in the y directionlarge-residual outlier
Far in the x directionhigh-leverage observation
Substantially changes the fitted resultinfluential observation
4

Example

When should both plots be used?

Viewing relationships and distributions together helps reveal group structure

Scatterplots by group

An overall positive relationship may weaken or reverse after separating the data into groups. Use color or panels to check subgroup patterns.

Use a scatterplot to inspect the two-variable relationship, then grouped boxplots to compare the outcome's median, IQR, and potential outliers across groups.

Caution

Overlapping boxes or whiskers do not prove that two groups are statistically identical. These plots support exploration; formal inference requires a separate analysis.

5

Interactive

Change the relationship and outlier

Change the relationship and outlier

First change the direction of the relationship and inspect the scatterplot pattern. Then move the final observation sharply and compare how the correlation and boxplot outlier flag respond.

Correlation

0.91

Potential outliers

0

Scatterplot

Boxplot

Q1 27.0Median 36.0Q3 38.0

What to notice

One observation can alter both the apparent relationship in a scatterplot and the outlier display in a boxplot, but the two plots emphasize different information.

Key takeaways

  • Scatterplots reveal direction, form, strength, groups, and unusual observations for two numeric variables.
  • Boxplots compress one distribution around its median and IQR.
  • Points beyond the whiskers are potential outliers to investigate, not automatic deletion candidates.
  • Using both plots supports safer interpretation of relationships and group distributions.
Next: Data Visualization & Graph Reading