What is important to know about Scatter graphs & correlation?

Positive correlation: as one variable increases, the other increases.

What is important to know about Scatter graphs & correlation?

Negative correlation: as one variable increases, the other decreases.

What is important to know about Scatter graphs & correlation?

No correlation: no clear pattern between the variables.

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Statistics & Probability · Foundation & Higher

Scatter graphs & correlation

Scatter graphs plot two variables to see if there is a relationship (correlation) between them. You need to describe the type and strength of correlation and draw lines of best fit.

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Key facts to remember

1Positive correlation: as one variable increases, the other increases.
2Negative correlation: as one variable increases, the other decreases.
3No correlation: no clear pattern between the variables.
4Correlation can be strong (points close to a line) or weak (points scattered widely).
5Correlation does not imply causation — two things may be related without one causing the other.
6An outlier is a point that does not fit the general pattern.

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Worked examples

Example 1

A scatter graph of temperature vs ice cream sales shows points rising from bottom-left to top-right. Describe the correlation.

Working

As temperature increases, ice cream sales increase
Points rise from bottom-left to top-right → positive correlation
Points are close together → strong positive correlation

AnswerStrong positive correlation

Example 2

A scatter graph shows hours of revision vs exam errors. Describe the expected correlation.

Working

More revision → fewer errors
One increases while the other decreases → negative correlation

AnswerNegative correlation

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Common mistakes

✗Confusing positive and negative correlation — positive means both go up together.

✗Stating that a correlation proves one variable causes the other.

✗Joining plotted points with a line instead of drawing a line of best fit.

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Exam tips

✓Use precise language: "strong positive correlation" not just "positive".

✓Correlation describes the relationship, not causation — avoid saying one variable "causes" the other.

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