AC9M7ST02 Year 7 Mathematics

This Content Descriptor from Year 7 Mathematics provides the specific knowledge and skills students should learn. Use it to plan lessons, create learning sequences, and design assessments that align with the Australian Curriculum v9.

create different types of numerical data displays including stem-and-leaf plots using software where appropriate; describe and compare the distribution of data, commenting on the shape, centre and spread including outliers and determining the range, median, mean and mode

Elaborations

• 1 using ordered stem-and-leaf plots to record and display numerical data collected in a class investigation, such as constructing a class plot of height in centimetres on a shared stem-and-leaf plot for which the stems \(12, 13, 14, 15, 16\) and \(17\) have been produced
• 2 comparing variation in attributes by category using split stem-and-leaf plots or dot plots; interpreting the shape of the distribution using qualitative terms to describe symmetry or skewness, “average” in terms of the mean, median and mode, and the amount of variation based on qualitative descriptions of the spread of the data
• 3 connecting features of the data display; for example, highest frequency, clusters, gaps, symmetry or skewness, to the mode, range and median, and the question in context; describing the shape of distributions using terms such as "positive skew", "negative skew", "symmetric" and "bi-modal" and discussing the location of the median and mean on these distributions
• 4 using mean and median to compare data sets, identifying possible outliers and explaining how these may affect the comparison; recognising how different displays make specific information about data more evident, including proportions, and measures of mean, mode or median, spread and extreme values; understanding that the median and the mean will be the same or similar for symmetric distributions but different for distributions that are skewed
• 5 comparing the mean and median of data with and without extremes; for example, estimation of standard measures for length or mass, informally considering for a given set of data what might constitute an unexpected, unusual or extreme data value
• 6 exploring how artificial intelligence systems can also use descriptive statistics to identify outliers or anomalies in data; for example, fraud detection and quality control

Related Achievement Standards