Processing Investigations – analysing and concluding Level 1


What type of graph will we use to represent the data?

Data relating to one variable can be represented as pictographs, bar and column graphs, ordered leaf and stem plots. Dot plots are also useful to represent data for relatively small data sets. These types of data representations help to demonstrate the range of data including gaps, clusters, outliers, mode and, to some extent, the median. Pie charts are useful to represent the relative or proportional composition of a total amount, but have limitations when the components are numerous and/or represent small portions of the whole.

The type of graph that is generated from the data depends on the type of data. In ‘fair test’ investigations students are investigating the relationship between two variables (that is. bivariate data).

For more advanced students you could distinguish continuous from discontinuous data. Discontinuous data is information about a characteristic that an object either possesses or does not possess. Blood type (either O, A, B or AB), year of birth or gender are discontinuous data sets. Each option is discrete and needs to be represented as such. Discontinuous data is usually represented in a column graph (vertical columns), bar graph (horizontal columns) or pie chart.

Continuous data is information about a characteristic that can be represented numerically and varies continuously such as height, weight, hand span, performance in a timed task. This type of data is often represented on a scatter plot or line graph. (see Integrating ICT learning pathway 1 for more information on how to use Excel to create such a graph). Once the graph has been constructed it can be used for interpolation or extrapolation (see level 2 pathway). The line constructed in a line graph does not necessarily join all the points, but usually is a ‘line of best fit’ indicating the general trend in the relationship. If there is no apparent relationship, the points may be represented as a scatter plot. Scatter plots can be used when the data set is really large, such as graphs of state-wide student test scores or size and brightness of stars.