Competences

• • a)Cooperate with the mathematics head of department to determine which manner is most suitable to prepare and present the data handling curriculum;
  • b)Develop and analyse of quantitative research studies;
  • c)Participate in the evaluation, review, and/or creation of school policies which require the interpretation, analysis, and presentation of quantitative data;
  • d)

    Solve problems related to univariate data, including

    • summarising and displaying data
    • investigating central tendency
    • locating percentiles
    • investigating spread;
  • e)

    Solve problems related to bivariate data, including

    • Reduction of a relationship to linear form (including using logarithms),
    • finding coefficients of linear regression, and
    • finding the coefficient of correlation;
  • f)Evaluate statistical findings from hypothesis testing and confidence interval analysis to support data-driven decision-making in educational settings, considering the reliability and limitations of the results.

Knowledge

• • • a)Summarise univariate data by organising and displaying data through frequency tables, stem-and-leaf displays, pie charts, bar charts and histograms, recognising how each method provides insight into data distribution;
    • b)Comprehend the techniques for analysing univariate data using measures of location, including the mean, weighted mean, mode, median, percentiles, and quartiles and understand how to accurately determine these measures in both raw and grouped data;
    • c)Gain knowledge of methods for analysing univariate data using measures of spread, such as the range, interquartile range, variance and standard deviation, to assess the dispersion and variability within datasets;
    • d)Explain the method of summarising bivariate data through scatter plots appreciating how this visualization technique reveals relationships and patterns between two variables;
    • e)Describe the methods for analysing bivariate data through measures of regression and correlation including how to apply and interpret regression analysis to model relationships between two variables and how to use correlation coefficients to assess the strength and direction of these relationships;
    • f)Explain the theoretical foundations of hypothesis testing;
    • g)Describe the concept of confidence intervals, including how they are constructed and interpreted.

Skills

• • • a)Summarise and display univariate data by presenting it in frequency tables, stem-and-leaf diagrams, pie charts, bar charts, and histograms;
    • b)Analyse the central tendency of univariate data by finding the mean, median and mode and decide which is most relevant for the given set of data;
    • c)Locate percentiles, fractiles, and quartiles in univariate data, and display quartiles in a box-and-whiskers plot;
    • d)Analyse the spread of univariate data by finding the range and interquartile range, variance and standard deviation using the relevant formulae;
    • e)Summarise and display bivariate data by drawing a scatter plot;
    • f)Analyse bivariate data by finding regression and correlation coefficients;
    • g)Draw conclusions and make predictions by interpreting the results obtained in (a) – (f);
    • h)Apply appropriate statistical techniques to perform hypothesis testing;
    • i)Construct and interpret confidence intervals for means, proportions, and differences between groups using both manual and software-based methods.