
- Mathematics General
- Year 12
Bivariate data analysis
approx. 12 hrs
8 topics
17 concepts
Develop proficiency in Bivariate data analysis to achieve a high level of academic outcomes.
approx. 12 hrs
8 topics
17 concepts
Develop proficiency in Bivariate data analysis to achieve a high level of academic outcomes.
Bivariate data analysis introduces students to some methods for identifying, analysing and describing associations between pairs of variables, including using the least-squares method as a tool for modelling and analysing linear associations. Students will develop proficiency to conduct regression analysis, with the aid of a CAS calculator. We will help students to effectively communicate results and make predictions using appropriate terminology.
This learning program is made up of the following 8 topics, broken down into 17 concepts.
Implement the statistical investigation process to answer questions
Identify independent and dependent variables in bivariate data analysis
Identify and describe associations between two categorical variables from a two-way table
Use box plots, dot plots and stem plots to identify and describe associations between a numerical and a categorical variable
Construct a scatterplot using technology
Construct a scatterplot using technology
Interpret scatterplots including direction, form and strength
Calculate the coefficient of determination and interpret its value
Calculate Pearson's correlation coefficient using technology
Demonstrate understanding of correlation and causation
Classify the correlation coefficient from a scatterplot or given a value for r
Use a residual plot to determine the appropriateness of using the equation of the least squares line to model the association
Calculate residuals
Fitting a least squares line and calculating the correlation coefficient
Interpolation and extrapolation of regression lines
Find the equation of the least-squares line of best fit
Interpret the slope and intercept of a regression line
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