The basic ideas of mathematical modeling as applied in probability and statistics, including the language and vocabulary used.

Find out how to display data using box and whisker plots, stem and leaf diagrams or histograms, and how to summarise data with measures of central location (mean, median, mode and quartiles) or measures of dispersion (range, interquartile range, variance and standard deviation), together with ideas of outliers and skewness. Use your graphic display calculator (GDC) to help speed up the calculations. See the Statistics diagrams page for a summary of the common diagrams used and use the Calculations page to practise using your calculator to find the summary statistics.

Find out about basic set theory and the use of Venn diagrams to represent data. There are also numerous simulations of probability experiments to enable you to get a feel for how probability works. Finally investigate mutually exclusive or independent events and conditional probability.

Understand dependent variables (response variables) and independent variables (explanatory variables). Find out about correlation - the degree to which two sets of data are linearly connected, and about regression equations - where the dependent variable in the regression equation is modeled as a function of the independent variables. Use the least squares line of best fit to make predictions about data. Find out how to use your graphic display calculator (GDC) to speed up the process.

Find out about discrete random variables and distributions. Investigate the expectation and variance of such variables and linear functions of the variable.
Investigate the uniform (or rectangular) distribution.

The normal distribution, also called the Gaussian distribution, is an important family of continuous probability distributions, applicable to many naturally occurring phenomena, such as the heights and weights of animals and plants. Its graph is often referred to as the bell curve.
The normal distribution is the most widely used family of distributions in statistics.