VCE General Mathematics Units 1 and 2 – Statistics

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6.1 Descriptive Statistics
6.2 Representing Data
6.3 Exploring data
6.4 Linear Modelling

Additional information


Investigating and comparing data distributions
Key knowledge
• types of data, including categorical (nominal or ordinal) or numerical (discrete and continuous)
• the concept of data distribution and its display using a statistical plot
• the five number summary
• mean x and standard deviation
Key skills
• construct and interpret graphical displays of data, describe the distributions of the variables involved and interpret in the context of the data
• calculate the values of appropriate summary statistics to represent the centre and spread of the distribution of a numerical variable and interpret in the context of the data
• construct and use parallel boxplots or back-to-back stem plots (as appropriate) to compare the distribution of a numerical variable across two or more groups in terms of centre (median), spread (IQR and range) and outliers, interpreting any observed differences in the context of the data.

Investigating relationships between two numerical variables
Key knowledge
• the response and explanatory variables and their role in modelling associations between two numerical variables
• scatterplots and their use in identifying and describing the association between two numerical variables
• the correlation coefficient r as a measure of the strength of a linear association, and the concepts of correlation and causation
• the equation of a fitted line.
Key skills
• use a scatterplot to describe an observed association between two numerical variables in terms of direction, strength and form
• estimate the value of the correlation coefficient r from a scatterplot and calculate its value from the data using technology
• identify the explanatory variable and use the equation of the least squares line fitted to the data to model an observed linear association
• calculate the intercept and slope correct to a specified number of decimal places or significant figures, and interpret the slope and intercept of the model in the context of data
• use the model to make predictions, being aware of the limitations of extrapolation.

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