Relationships

It is important to be able to identity relationships in data. This allows trends to be recognised and may allow for predictions to be made. Relationships in data can be identified in several ways.

Scatter plots

These graphs show the relationship between two sets of data, eg a person's height and weight.

A scatter graph showing a positive correlation has points close together, increasing along both axes.

A line of best fit or trend line can be added to the scatter plot to show the relationship between the two variables. When drawing a line of best fit or trend line it is important to have as many points as possible going through the line.

A strong correlation is when the points on the scatter graph lie very close to the line of best fit. With a strong correlation, the two variables are related to one another - as one changes, so does the other. A weak correlation is when the points lie far away from the line of best fit. In this case, the two variables are not necessarily related to one another - a change in one does not lead to a change in the other.

A line of best fit is a line drawn through points in a way that goes through the majority of the points.

Interpolate trends

This is when a value is found within the data set, using the line of best fit. The value was not originally plotted, but can be read off the line of best fit.

Interpolation is drawing an imaginary line up to the line of best fit, then drawing another imaginary line along the other axis to find a value.

Extrapolate trends

This is when a value is found outside of the data set. Extrapolation may provide uncertain results as it is based on extending the line of best fit beyond a known set of data.

Extrapolation is extending the line of best fit beyond the observed data, and using that to find a value.