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Topic 2: Bivariate data analysis 2

Fitting a linear model to numerical data

6 hours

• Model a linear relationship by using technology to fit a least-squares line to the data, in the form of = + where is slope (gradient) and is -intercept. line, where is slope (gradient), is correlation coefficient, is (sample) standard deviation --- --- --- --- of values, is (sample) standard deviation of values, is -intercept, is mean of values and is mean of values. 1 interactive
• Understand and use = and = − to determine the equation of a least-squares line, where is slope (gradient), is correlation coefficient, is (sample) standard deviation of values, is (sample) standard deviation of values, is -intercept, is mean of values and is mean of values. 1 interactive
• Construct a residual plot and use it to assess the appropriateness of fitting a linear model to the data. 1 interactive
• Interpret the -intercept and slope (gradient) of the fitted line.
• Distinguish between interpolation and extrapolation. 1 interactive
• Use the equation of the least-squares line to make predictions.
• Recognise and explain the potential dangers of extrapolation.

Association and causation

6 hours

• Recognise and explain that an observed association between two variables (categorical and/or numerical) does not necessarily mean that there is a causal relationship between them. 1 interactive
• Identify and communicate possible non-causal explanations for an association, including coincidence or the influence of another variable. 1 interactive
• Solve practical problems by identifying, analysing and describing associations between two variables (categorical and/or numerical).