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How do you plot a scatter graph, describe correlation and use a line of best fit to make predictions?

Plot and interpret scatter graphs, describe the type and strength of correlation, draw a line of best fit and use it to estimate values, and understand that correlation does not imply causation.

A focused answer to the WJEC GCSE Mathematics statistics content on scatter graphs, covering plotting bivariate data, describing positive, negative and no correlation, drawing and using a line of best fit for predictions, and the limits of interpolation and extrapolation.

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  1. What this dot point is asking
  2. Plotting and describing correlation
  3. The line of best fit
  4. Making and judging predictions
  5. Correlation is not causation
  6. Why this matters

What this dot point is asking

WJEC asks you to plot bivariate data on a scatter graph, to describe the correlation between the two variables, to draw a line of best fit and use it to estimate values, and to understand that correlation does not prove that one variable causes the other. The exam rewards describing correlation precisely (type and strength), reading predictions off the line, and judging when such a prediction is reliable. It is examined on both components and connects statistics to the straight-line graphs of algebra.

Plotting and describing correlation

A scatter graph reveals whether two quantities are related.

So height against shoe size usually shows positive correlation, while a car's age against its value shows negative correlation. Describe both the type and the strength in the exam.

The line of best fit

A line of best fit summarises the trend with a straight line.

Draw a single straight line that follows the trend with roughly as many points above it as below, passing through the middle of the data (it often passes near the mean point). It need not pass through the origin or any particular point. The line lets you estimate the yy value for a given xx (or vice versa): read up to the line and across to the axis.

Making and judging predictions

A prediction's reliability depends on where it lies.

This is a favourite "explain why" mark: a prediction far beyond the data is unreliable because it is extrapolation.

Correlation is not causation

A relationship in the data does not prove cause and effect.

Why this matters

Scatter graphs are reliably examined for a few high-value skills: describing correlation in context, drawing and using a line of best fit, and judging a prediction's reliability with the right vocabulary (interpolation, extrapolation, causation). These are AO2 and AO3 reasoning marks, and the topic links statistics to the algebra of straight-line graphs, since the line of best fit is a y=mx+cy = mx + c trend line. Precise wording, not just "positive" but "positive correlation, so more X is associated with more Y", is what earns full marks.

Exam-style practice questions

Practice questions written in the style of WJEC exam questions on this dot point, with worked answer explainers. The year tag is the paper they imitate, not the source.

WJEC 20182 marksA scatter graph plots hours of revision against test score for a class, and the points rise from bottom-left to top-right in a clear band. Describe the correlation and what it means. (Foundation and Higher, Unit 2, calculator.)
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Points rising from bottom-left to top-right show positive correlation, and a clear band means it is strong.

In context: students who revised for longer tended to score more highly.

Markers award a mark for "positive correlation" and a mark for interpreting it in context (more revision is associated with higher scores). Simply writing "positive" without the meaning may not earn the second mark.

WJEC 20213 marksOn a scatter graph with a line of best fit, a student reads off the predicted value for an xx far beyond all the plotted data. Estimate the value as instructed, then state why this estimate may be unreliable. (Foundation and Higher, Unit 2, calculator.)
Show worked answer →

Read up from the given xx value to the line of best fit, then across to the yy-axis to estimate the value (the exact figure depends on the line drawn).

The estimate may be unreliable because the xx value lies outside the range of the data, so this is extrapolation; the pattern is not known to continue beyond the data collected.

Markers give a mark for using the line of best fit to read off the estimate and a mark for explaining that extrapolation outside the data range is unreliable. Saying only "it might be wrong" without naming extrapolation loses the reasoning mark.

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