MA32-10 Maths Coming soon
Line of best fit, correlation vs causation, and prediction
MA32-10
This lesson is coming soon.
In this lesson
Draw a line of best fit, use it to predict a value, know correlation does not imply causation, and explain why extrapolating beyond the data range is unreliable using the correct reasoning.
What it covers
- Draw a line of best fit through plotted bivariate data
- Use the line to read off/predict a value within the data range (interpolation)
- Know that correlation does not imply causation
- Explain why extrapolating beyond the data range is unreliable, using the 'outside the range of the data' argument specifically
Key words
For: AQA GCSE 8300, Edexcel GCSE 1MA1, Eduqas GCSE C300, OCR GCSE J560
On the specification
| Board | Spec | Statement |
|---|---|---|
| AQA GCSE 8300 | S6 | Use and interpret scatter graphs of bivariate data |
| Edexcel GCSE 1MA1 | S6 | Use and interpret scatter graphs of bivariate data; recognise correlation and know that it does not indicate causation; draw estimated lines of best fit; make predictions; interpolate and extrapolate apparent trends while knowing the dangers of so doing |
| Eduqas GCSE C300 | FS6 | Use and interpret scatter graphs of bivariate data; recognise correlation and know that it does not indicate causation; draw estimated lines of best fit; make predictions; interpolate and extrapolate apparent trends whilst knowing the dangers of so doing |
| Eduqas GCSE C300 | HS7 | Use and interpret scatter graphs of bivariate data; recognise correlation and know that it does not indicate causation; draw estimated lines of best fit; make predictions; interpolate and extrapolate apparent trends whilst knowing the dangers of so doing |
| OCR GCSE J560 | 12.03c | Plot and interpret scatter diagrams for bivariate data. Recognise correlation. |
For teachers
This GCSE Maths lesson teaches line of best fit, correlation vs causation, and prediction. By the end, students should be able to draw a line of best fit, use it to predict a value, know correlation does not imply causation, and explain why extrapolating beyond the data range is unreliable using the correct reasoning. It works through three worked examples and the mistakes examiners report.