Be able to interpret tables and diagrams for single-variable data.
e.g. vertical line charts, dot plots, bar charts, stem-and-leaf diagrams, box-and-whisker plots, cumulative frequency diagrams and histograms (with either equal or unequal class intervals). Includes non-standard representations.
#2.02b
Understand that area in a histogram represents frequency.
*Includes the link between histograms and probability distributions.
Includes understanding, in context, the advantages and disadvantages of different statistical diagrams.*
#2.02c
Be able to interpret scatter diagrams and regression lines for bivariate data, including recognition of scatter diagrams which include distinct sections of the population.
*Learners may be asked to add to diagrams in order to interpret data, but not to draw complete scatter diagrams.
Calculation of equations of regression lines is excluded.*
#2.02d
Be able to understand informal interpretation of correlation.
#2.02e
Be able to understand that correlation does not imply causation.
#2.02f
Be able to calculate and interpret measures of central tendency and variation, including mean, median, mode, percentile, quartile, inter-quartile range, standard deviation and variance.
*Includes understanding that standard deviation is the root mean square deviation from the mean.
Includes using the mean and standard deviation to compare distributions.*
#2.02g
Be able to calculate mean and standard deviation from a list of data, from summary statistics or from a frequency distribution, using calculator statistical functions.
*Includes understanding that, in the case of a grouped frequency distribution, the calculated mean and standard deviation are estimates.
Learners should understand and be able to use the following formulae for standard deviation:
n∑(x−xˉ)2=n∑x2−xˉ2,
∑f∑f(x−xˉ)2=∑f∑fx2−xˉ2.
Formal estimation of population variance from a sample is excluded. Learners should be aware that there are different naming and symbol conventions for these measures and what the symbols on their calculator represent.*
#2.02h
Recognise and be able to interpret possible outliers in data sets and statistical diagrams.
#2.02i
Be able to select or critique data presentation techniques in the context of a statistical problem.
#2.02j
Be able to clean data, including dealing with missing data, errors and outliers.
*Learners should be familiar with definitions of outliers:
more than 1.5× (interquartile range) from the nearer quartile,
more than 2× (standard deviation) away from the mean.*