1MA1 · S6StatisticsFoundation & HigherGrades 2–5

Scatter Graphs

Do taller people have bigger feet? Do cars lose value as they get older? A scatter graph\textbf{scatter graph} plots pairs of values as points to reveal whether two variables are related. If they are, a line of best fit lets you make predictions — as long as you know when those predictions can be trusted.

40 min Statistics

The big picture

The pattern of points describes the correlation\textbf{correlation}: positive if both variables increase together, negative if one increases as the other decreases, none if there is no clear pattern. How closely the points cluster around a line shows how strong the relationship is.

Edexcel asks you to describe correlation, draw a line of best fit, use it to estimate values, and evaluate those estimates. The two key cautions are that predictions outside the data range are unreliable, and that correlation does not prove one variable causes the other.

What you'll learn

  • Plot scatter graphs and identify outliers
  • Describe the type and strength of correlation
  • Draw a line of best fit and use it to estimate values
  • Explain interpolation, extrapolation and the difference between correlation and causation
1

Plotting and outliers

Each pair of values gives one point: the explanatory variable on the xx-axis, the response on the yy-axis.

An outlier\textbf{outlier} is a point that does not fit the pattern of the rest. It might be a recording error or a genuine unusual case, and is usually ignored when drawing a line of best fit.

2

Correlation

Positive correlation\textbf{Positive correlation}: as one variable increases, the other tends to increase — height and shoe size.

Negative correlation\textbf{Negative correlation}: as one increases, the other tends to decrease — a car’s age and its value.

No correlation\textbf{No correlation}: no clear relationship — height and exam score.

Strength describes how close the points are to a straight line: strong, moderate or weak.

Your turnSame method, new numbers — try it before moving on.

What type of correlation would you expect between hours of sunshine and ice-cream sales? Type positive, negative or none.

Show worked solution
Solution
  1. 1More sunshine tends to mean more ice creams sold.
  2. 2Both increase together: positive.

Answer: positive

Tip — Describe correlation in words that fit the context as well as the type: "strong negative correlation: older cars tend to be worth less".

3

Lines of best fit

A line of best fit is a single straight line that follows the trend, with roughly equal numbers of points on each side. It does not have to pass through the origin or through any particular point.

Ignore outliers when drawing it.

To estimate a value, read up from the known value to the line, then across.

Example 1A line of best fit for revision hours (xx) against test score (yy) passes through (2,40)(2, 40) and (10,80)(10, 80). Estimate the score for 6 hours of revision, and the gradient’s meaning.
1Gradient: 80−4010−2=5\dfrac{80 - 40}{10 - 2} = 5.
2At x=6x = 6: 40+5×4=6040 + 5 \times 4 = 60.
AnswerAbout 60 marks; each extra hour of revision is associated with about 5 more marks.
Your turnSame method, new numbers — try it before moving on.

A line of best fit passes through (2,40)(2, 40) and (10,80)(10, 80). What is its gradient?

Show worked solution
Solution
  1. 180−4010−2=408=5\frac{80 - 40}{10 - 2} = \frac{40}{8} = 5.

Answer: 55

4

Reliability and causation

Interpolation\textbf{Interpolation} — estimating within the range of the data — is fairly reliable when the correlation is strong.

Extrapolation\textbf{Extrapolation} — estimating outside the range — is unreliable, because the trend may not continue. The revision line above would predict 130 marks for 20 hours, which may be impossible.

Correlation does not mean causation\textbf{Correlation does not mean causation}. Ice-cream sales and sunburn are positively correlated, but ice cream does not cause sunburn — hot weather affects both.

Why this works

A hidden third variable explaining both is very common. Before claiming "xx causes yy", ask whether something else could be driving both.

Think like an examiner

  • Describe correlation type and interpret it in context.
  • Draw a ruled line of best fit ignoring outliers.
  • Comment on reliability, mentioning extrapolation where relevant.

Scatter graphs

positive, negative or no correlation\text{positive, negative or no correlation}
Types of correlation.
interpolation: within the data range\text{interpolation: within the data range}
More reliable.
extrapolation: outside the data range\text{extrapolation: outside the data range}
Unreliable.

Watch out for these

Forcing the line of best fit through the origin.
It follows the trend of the points, wherever that is.
Joining the points dot to dot.
Draw one straight line through the trend.
Saying correlation proves cause.
Correlation shows association only.
Trusting a prediction far outside the data.
Extrapolation is unreliable.

Stretch yourself

Data for 12 towns show strong positive correlation between the number of doctors and the number of deaths per year. A newspaper claims that doctors cause deaths. Explain why this is wrong, and suggest a better explanation.

Hint — Think about a variable that affects both the number of doctors and the number of deaths.

Reveal solution
SolutionCorrelation does not show causation. Both variables are linked to a third one: the size of the town’s population. Larger towns have more doctors and, simply because more people live there, more deaths each year. Comparing deaths per 1000 people with doctors per 1000 people would be a fairer test of any real relationship. ■\blacksquare

Questions students ask

Does a line of best fit have to go through the origin?
No — only if the trend of the data does.
What makes a prediction reliable?
Strong correlation and a value within the range of the data (interpolation).
Should outliers be removed?
Ignore them when drawing the line of best fit, but consider whether they are errors or genuine unusual values.

Key takeaways

  • Scatter graphs show whether two variables are related.
  • Correlation can be positive, negative or none, and strong or weak.
  • Draw one straight line of best fit, ignoring outliers.
  • Interpolation is fairly reliable; extrapolation is not.
  • Correlation does not prove causation.

Exam-style questions

Practise it the way it is examined

3 original questions · 4 marks, written to match the style and mark allocation of the real papers. Work on paper, then open the mark scheme and tick the marks you earned — method marks count even if the final answer slips.

Question 1Grade 21 mark
  1. Describe the type of correlation you would expect between the age of a car and its value.[1]
    Show mark scheme
Answer on paper first, then mark it.
Question 2Grade 42 marks

A line of best fit for test score yy against revision hours xx has equation y=4x+30y = 4x + 30.

  1. (a)Estimate the score for 7 hours of revision.[1]
    Show mark scheme (a)

    Answer: 5858

  2. (b)Interpret the gradient in context.[1]
    Show mark scheme (b)
Answer on paper first, then mark it.
Question 3Grade 51 mark
  1. The data covers 1 to 10 hours of revision. Explain why the line should not be used to estimate the score for 25 hours.[1]
    Show mark scheme
Answer on paper first, then mark it.

Test yourself

Answer a few questions, then review anything you missed.

Next step

Keep practising

Start with the quiz above to check the key ideas. You can also choose another topic from the practice library.

Browse practice topics

Related lessons

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