# Level 3

## 91581: Investigate bivariate measurement data

Updated May 2019. The sections dealing with ‘Investigating bivariate measurement data’ and ‘Required quality of student response’ have been updated. The section ‘Identifying features in the data’ has been edited.

Students need to provide evidence of each component of the statistical enquiry cycle detailed in Explanatory Note 3 of the standard.

It is possible for a student to meet the criteria for all grades by considering an appropriate linear model.

### Investigating bivariate measurement data

Sufficient time needs to be allocated for students to research the context and acquire appropriate and relevant contextual knowledge. Students need to identify a purpose and pose a relationship question which is informed by this contextual knowledge. The conclusion needs to include an answer to the relationship question that has been posed.

Students may choose to split or re-categorise the data as part of the investigation. The initial data set should be sufficiently large to ensure that the subsets of data that may be investigated are large enough to allow a meaningful investigation.

### Identifying features in the data

Students need to create a scatter graph and use a visual inspection to describe features in the data, before fitting a model. Features need to include the strength and direction of the relationship, and could include clusters and unusual values, and whether a linear model is appropriate. Students need to take care to justify the existence of any unusual value/outlier with reference to the data set and the context.

### Using the model to make a prediction

Students need to make a prediction, in context, for at least one value of the explanatory variable. The precision of the prediction could be discussed by reviewing the strength of the relationship and the scatter on the graph close to the relevant explanatory data value. It is not sufficient to produce a table of values for the prediction.

### Required quality of student response

For Merit, students need to justify all findings with reference to evidence from the displays and statistics and link the purpose and findings to their research.

For Excellence, students need to integrate the statistical and contextual knowledge, gained from their research, throughout the investigation process. This may include reflecting on the process, considering other relevant variables, evaluating the adequacy of the model(s), or showing a deeper understanding of the model(s).