An outlier is a data point that is extremely large or small compared to the rest of the dataset. Visually, outliers lie far away from where the majority of the results are clustered.

Outliers in scatter graphs

Maths

Checking understanding of scatter graphs

Checking understanding of correlation

Estimating from scatter graphs

Interpolation versus extrapolation

Constructing time series graphs

Interpreting time series graphs

Problem solving with scatter graphs and time series

In the prior unit, pupils explored different graphs and charts as a way of presenting data visually. Pupils were introduced to scatter graphs and learnt how to construct them. In this unit, this understanding is extended to using a line of best fit to allow interpolation.

In this unit pupils will learn how to interpret and extract data from scatter graphs and time series graphs.

Graphical representations of data: scatter graphs and time series

An {{ }} is a data point that is extremely large or small compared to the rest of the dataset.

No, all datasets will have a highest value.

No, there will always be a datapoint that is lower than the rest.

No, not all the data points will cluster.

Yes, there may be no points that are far away from the main cluster of data.

Sam says that it is possible to have a dataset with no outliers. Is Sam correct?

town with a low number of children and many elderly people

town with a high number of children and many elderly people

town with a high number of children and few elderly people

town with a low number of children and few elderly people

The scatter graph shows information about number of children and number of elderly people in a set of towns. Each point represents a town. Match each outlier to the correct statement.

It depends, you should investigate each outlier carefully.

No, if a data point is extremely large then it must be an error.

Yes, an outlier is always just an unusual result in the data.

Sofia says you should never ignore an outlier. Is Sofia correct?

No, outliers are always too far from the rest of the data to show.

Yes, you may need to zoom in on the graph to use interpolation.

No, removing it can make the trend of the data easier to see.

Yes, otherwise the scatter graph is wrong.

Yes, the outlier may help highlight useful information.

Jacob says you should show the outlier on a scatter graph. Is Jacob correct?

Extrapolation is when you estimate values that are {{}} the range of available data

Which values are obtained by interpolation on this scatter diagram?

Select the regions where extrapolation can be used to make estimations on this scatter graph.

No, interpolation is always completely unreliable.

No, it has to be used with caution as the trend might not continue.

Yes, interpolation is always completely reliable.

Yes, but you can only make accurate predictions about similar situations.

Alex says, “I can always use interpolation to make a valid prediction when my bivariate data has correlation.” Is Alex definitely correct?

Laura: I want the house price in Oakford where the median income is £19 000

Alex: I want the house price in Ashfield where the median income is £35 000

Jacob: I want the house price in Spain where the median income is £35 000

Sam: I want the house price in Elmway where the median income is £92 000

The scatter graph shows data taken from the ONS, where each point represents a region in England and Wales in 2023. Select which of these Oak pupils can use this graph to make an accurate prediction.

The scatter graphs shows information about house prices and income in different regions. Estimate the difference between the house prices in the North and South for a median income of £25 000

Outliers in scatter graphs

Lesson outcome