S1.2 - Representation and summary of data

Histograms, stem and leaf Using histograms, stem and leaf diagrams and box plots to diagrams, box plots. compare distributions.; Back-to-back stem and leaf diagrams may be required.; Drawing of histograms, stem and leaf diagrams or box plots will not be the direct focus of examination questions.

Use histograms, stem and leaf to connect the rule to the data and decision in the question.

This matters because histograms, stem and leaf determines what can be inferred or chosen; begin with the stated conditions and keep the conclusion tied to the evidence.

Example: apply histograms, stem and leaf to one small, clearly defined case, show the key step or comparison, and explain the result in words.

Boundary: Histograms, stem and leaf is not a universal recommendation. Check the syllabus scope, assumptions, units and the limits of the evidence before generalising.

Measures of location – mean, Calculation of mean, mode and median, range and median, mode. interquartile range will not be the direct focus of examination questions.; Students will be expected to draw simple inferences and give interpretations to measures of location and dispersion.; Significance tests will not be expected.; Data may be discrete, continuous, grouped or ungrouped.; Understanding and use of coding.

Use measures of location – mean, to connect the rule to the data and decision in the question.

This matters because measures of location – mean, determines what can be inferred or chosen; begin with the stated conditions and keep the conclusion tied to the evidence.

Example: apply measures of location – mean, to one small, clearly defined case, show the key step or comparison, and explain the result in words.

Boundary: Measures of location – mean, is not a universal recommendation. Check the syllabus scope, assumptions, units and the limits of the evidence before generalising.

Measures of dispersion – variance, Simple interpolation may be required.; Interpretation of standard deviation, range and measures of location and dispersion. interpercentile ranges.

Use measures of dispersion – variance, to connect the rule to the data and decision in the question.

This matters because measures of dispersion – variance, determines what can be inferred or chosen; begin with the stated conditions and keep the conclusion tied to the evidence.

Example: apply measures of dispersion – variance, to one small, clearly defined case, show the key step or comparison, and explain the result in words.

Boundary: Measures of dispersion – variance, is not a universal recommendation. Check the syllabus scope, assumptions, units and the limits of the evidence before generalising.

Skewness.; Concepts of outliers.; Students may be asked to illustrate the location of outliers on a box plot.; Any rule to identify outliers will be specified in the question.

Use skewness to connect the rule to the data and decision in the question.

This matters because skewness determines what can be inferred or chosen; begin with the stated conditions and keep the conclusion tied to the evidence.

Example: apply skewness to one small, clearly defined case, show the key step or comparison, and explain the result in words.

Boundary: Skewness is not a universal recommendation. Check the syllabus scope, assumptions, units and the limits of the evidence before generalising.