AC9M10ST03

Elaborations

• AC9M10ST03_E1discussing the difference between association and cause and effect, and relating this to situations such as health, diversity of species and climate control
• AC9M10ST03_E2using 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
• AC9M10ST03_E3informally using a line of good fit by eye to discuss reliability of any predictions
• AC9M10ST03_E4exploring 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
• AC9M10ST03_E5investigating 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
• AC9M10ST03_E6investigating 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