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Students learn how to plot and interpret a scatter graph. Learning progresses from using the line of best fit to find missing values to understanding whether correlation means causation.

This unit takes place in Year 10 Term 1 for Foundation students and Year 9 Term 5 for Higher students. This unit follows on from statistical representation.

- Solve comparison, sum and difference problems using information presented in a line graph
- Interpret and present discrete and continuous data using appropriate graphical methods, including bar charts and time graphs.
- Work with coordinates in all four quadrants

- apply statistics to describe a population
- 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

- Scatter graphs need to be drawn on graph paper or using I.C.T to ensure accuracy and help identify the line of best fit.
- Two measurements are ‘associated’ if the points lie approximately along a straight line. This shows a linear relationship. However, an association between two variables can exist in a non-linear relationship.
- Correlation is used to describe the strength of a linear relationship between two variables. If no correlation exists (the points do not appear to follow a trend of direction) the two variables are considered to have no linear relationship.

- Students often have difficulty choosing a suitable scale to use for each axis. Encourage the use of graph paper to ensure the graph is appropriately scaled.
- When drawing the line of best fit by eye it should represent the directional trend of the data. It does not have to intersect the origin or travel through every point.
- Correlation does not always imply a causal relationship since other factors could contribute.

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Connecting simultaneous equations to density, volume, surface area and rearranging formula.

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How to teach plotting and interpreting histograms with unequal class widths.