Preparing Data for Analysis progression

The ability to work with data is an important part of using and understanding statistics in everyday life. Refer here for information about the development of the ability to reason statistically.

Find more information about the development of the ability to reason statistically.

Most adults will be able to:			Activities
1.	sort objects according to their attributes, organise data about the objects and represent data, using concrete objects or pictures.	Learners can: identify potentially relevant attributes of objects in a collection use a specific attribute as a basis for sorting a collection of objects draw a diagram to represent the objects that make up a collection, in their different categories.
3.	sort and organise category data and represent it, using tables, pictographs and bar graphs.	Learners can: identify and describe categories to be used as the basis for gathering data count data and sort it into categories order data by category or frequency represent category data, using tables, pictographs or bar graphs.	Sort, represent and interpret category data Learners sort and organise category data and represent it on graphs.
4.	sort, organise and represent data, using tables and graphs such as line plots, bar graphs and line graphs recognise the differences involved in representing category and numeric data.	Learners can: read and discuss numeric data presented in a simple table format distinguish between data based on a single variable and data based on two variables calculate the median and range of a data set represent data in one or two variables in the form of tables, line plots, bar graphs and stem-and-leaf plots.	Sort, represent and interpret number data Learners sort and organise number data and represent it on graphs.
6.	sort, organise, clean and represent multi-variate data, making appropriate use of histograms, stem-and-leaf plots, box plots (box-and-whisker diagrams) and scatter plots graph time-series data.	Learners can: clean a data set by eliminating irrelevant data or obviously erroneous data create a variety of graphical representations and determine which best shows the key features of the data.