• Chance and probability: considering probabilities as percentage chances, defining outcomes as compatible or incompatible (avoiding use of "mutually exclusive"), considering cases where addition of probabilities is appropriate and inappropriate; also use of multiplication of probabilities.
• Data gathering basics: basic experimentation, surveys, primary vs secondary sources, use of data base tools including Aspect Huntley and Connect4.
• Basic types of data and data presentation: qualitative and quantitative data, further defining quantitative data as rank, numeric or continuous; pie charts, bar charts, histograms, frequency polygons, shapes including skewed and symmetric; misuse of graphs in media; graphs for two linked columns - scatterplots, line charts, time series, shapes including sigmoid and exponential, seasonal fluctuations and long-term trends.
• Centre of a graph: average, percentiles (link with shapes of graphs).
• Spread of a graph: standard deviation to be considered as an average squared distance from the centre; empirical rule used to explain use of standard deviation.
• A bell shaped curve: common real world examples of graphs which are bell shaped, counter examples (such as queue waiting times) to reinforce need to investigate graph shapes.
• How the centres of many graphs together behave (CLT): covered only as a fact that centres vary between data sets, but spread decreases as number of observations increase.
• Testing as a concept: avoiding use of jargon and terminology - two competing ideas, one of which must be true; use of Excel for calculations; interpretation of p-value as "chance that idea is wrong", and conclusion in terms of which idea we decide to use.
• Specific tests to be covered: 1 sample Z, 1 sample T, 2 sample T, ANOVA.
• Simple Regression: Discussion of cause and effect, common cause and spurious relationships; correlation, dependent and independent variables, slope and intercept in practical terms (what happens when dependent variable is set to zero, what happens when dependent variable is increased by one)   concept of the "line of best fit", calculations in Excel leading to interpretations about existence (or not) of a linear relationship.

Students who are successful in completing this course are expected to obtain the following skills:

• The ability to interpret statistical analysis tools commonly used in the workplace;
• The ability to critically evaluate a standard business report including the graphics, probability statements and resultant commentary; and,
• Use of Excel for basic data manipulation and simple statistical and graphical analysis.

Assessable in-tutorial work, group assignment and, examination.

Please refer to Course Website