AC9M10ST03 – Year 10 Mathematics: Statistics
Content Descriptor
This content descriptor sits within Year 10 Mathematics. Use it to identify the learning students need, connect it with the achievement standard and plan suitable evidence of learning.
What this means for planning
The work in AC9M10ST03 is both graphical and interpretive. Students construct a scatterplot and describe the association between two numerical variables in terms of strength, direction and linearity. They also need to avoid treating association alone as proof of cause and effect.
A practical planning sequence
- Choose meaningful bivariate data: Use two numerical variables with enough observations and context for students to interpret the resulting pattern.
- Construct before commenting: Check scales, labels and plotted points, then use a consistent vocabulary for strength, direction and linearity.
- Interrogate the conclusion: Use a line of good fit or an informal prediction, then ask what the graph supports, what it does not prove and which observations need attention.
Evidence to look for
- A correctly constructed scatterplot with appropriate axes, scales and labels.
- A description that addresses strength, direction and linearity using evidence from the graph.
- A prediction or claim accompanied by a limitation, including appropriate caution about causation.
See the complete curriculum context
Open the achievement standard and all linked content descriptors together in the Year 10 Mathematics curriculum map.
Related item: Explore AC9M10M03: practical trigonometry.
A TeaCheese planning example
Use AC9M10ST03 in a TeaCheese planning brief to create a data investigation that separates graph construction, association language, prediction and critique.
Planning guides: How content descriptions work in the Australian Curriculum · Turn a content description into learning intentions and success criteria
Elaborations
- • Elaboration discussing the difference between association and cause and effect, and relating this to situations such as health, diversity of species and climate control
- • Elaboration using statistical evidence to make, justify and critique claims about association between variables, such as in contexts of climate change, migration, online shopping and social media
- • Elaboration informally using a line of good fit by eye to discuss reliability of any predictions
- • Elaboration exploring how scatter plots and association help data scientists gain insights into the data, identify relationships, and can be applied to machine learning to make informed decisions about feature engineering and assess model performance
- • Elaboration investigating artificial intelligence systems that analyse bivariate data to forecast or make predictions based on association using correlation analysis and discussing limitations; for example, the artificial intelligence may not capture the causality between variables or account for the contextual or ethical implications
- • Elaboration investigating the relationship between 2 variables of spear throwers used by First Peoples of Australia by using data to construct scatterplots, make comparisons and draw conclusions
Plan a lesson from this content description.
Start freeAchievement standard statements this supports
- They plan and conduct statistical investigations involving bivariate data.See it on the standard page →
- Students represent the distribution of data involving 2 variables, using tables and scatter plots, and comment on possible association.See it on the standard page →
- Students compare the distribution of continuous numerical data, using various displays, and discuss distributions in terms of centre, spread, shape and outliers.See it on the standard page →