Take for example a human resource manager who is interested in how three different departments in a business situation waste time on the internet on a given day when they should be doing company business. The human resource person would collect data through a time study process and determine the number of times each employee in each department logs on and off the internet for personal business. The times would be collected, added together and the times of each department converted to percentages. In the example presented, the human resource manager can report that, cumulatively, the employees in Department 1 spent a total of 5 hours a day on the Internet, Department 2 employees 2 hours a day and Department 3 spent 6 hours. The raw numeric count is then converted to percentages and the pie chart would look like the following (Ohlson, 2005):
The solution to the data presented above is calculated as follows:
The total (sum) of 5 and 2 and 6 occurrences is 13
http://www.algebra.com/cgi-bin/plot-formula.mpl?expression=5%2F%285%2B2%2B6%29&x=0001
or...
As such, pie charts are not an effective tool upon which to make statistical inferences or draw statistical conclusions with respect to any phenomenon, event, or situation being researched and tested. As stated earlier pie charts are visual presentations of data and act as a prelude to continued statistical data analysis (Weirs, 2005).
References
Ohlson, E.L. (1998). Best Fit Statistical Practices. Chicago: ACTS Testing Labs. p.43
Weirs, Ronald M. (2005). Introduction to Business Statistics. Scranton, PA: Brooks/Cole
Publishing Company.
Chart/Graph
