For Merit standard , learners will accurately solve problems for a data set involving
routine and non-routine central tendency, dispersion and probability distribution
operations, using industry standard software. For example, they will tabulate grouped
data and generate annotated pie charts, bar charts and histograms accurately. Learners
will accurately calculate representative values for central tendency (mean, mode and
median). They will calculate variance, standard deviation, range, interquartile and inter-
percentile values of normally distributed data. Learners will carry out a t-test on two sets
of data and draw a simple conclusion from the result.
They will use experimental data to accurately determine the equation of the linear
regression and correlation coefficient, predicting the value of the dependent variable for
a non-measured value. Learners will compare the value with the corresponding value
from the graph. They will select and use a suitable type of regression for a non-linear
relationship, such as a power relationship. Each calculation will be supported by a brief
explanation of the method used.
Overall, the numerical work will be to an appropriate degree of accuracy, as specified by
the assessor or appropriate for the chosen problems being solved, and the methods
selected will be used correctly. Solutions will contain an explanation of the process,
which will be logically structured and the correct mathematical terminology and relevant
units will be used. There may be a limited number of minor errors or omissions in non-
routine operations. For example, when evaluating a data set, learners may determine
the mean and standard deviation for a sample and find a degree of correlation between
samples, but not draw conclusions from the values.
For Pass standard , learners will solve problems for a data set involving routine central
tendency, dispersion and probability distribution operations, using industry standard
software. For example, they will tabulate data and generate pie charts, bar charts and
histograms. Learners will calculate representative values for central tendency (mean,
mode and median). They will calculate variance, standard deviation, range, interquartile
and inter-percentile values of normally distributed data. Learners will carry out a t-test
on two sets of data.
They will use software to solve problems for a data set involving routine linear
regression operations. Learners will use experimental data to determine the equation of
linear regression and correlation coefficient. They will use a regression technique for a
non-linear relationship, such as a power relationship.
Overall, the evidence will be logically structured and the correct methods will be used.
The evidence may contain some arithmetic errors that 'carry through', for example the
value of the mean from a data set may be incorrect but that value will be used correctly
to find the standard deviation. Minor errors and omissions are acceptable. For example,
the titles of axes on a histogram may be missing units. There will be an appreciation
of correct use of units but there may be errors or inconsistency in their application.
Learners will include evidence of simple checks to determine if numerical answers
are ’reasonable'.