we are going to look at sets of data and explore ways of analyzing that data so that conclusions can be made from that data. Now, it is possible to have a data set that has many as 100 data points or even 1000's of data points. We need to summarize this data in a way that makes the results more obvious and understandable. One way to summarize the data is by using a frequency table.

If you look at Table (Academy Awards) there is a lot of data. Granted it is organized (beginning with the first awards ceremony) BUT the results are not obvious at a glance.

Now look at Table Here the results are more direct. We can immediately see that most of the award winners were between the ages of 31 and 40. This is a simple FREQUENCY table.

• How many actresses were 41-50 years old when they won?
• How many actresses were 51-60 years old when they won?
• How many actresses were 41-60 years old when they won?

Now look at Table 2-3. This is the same information, but now expressed as a percentage. Relatively speaking, this table gives us an even better idea of the data. Now we know 39% of the actresses were between the ages of 31 and 40 when they received their awards. This is a RELATIVE FREQUENCY (%) table. Magazine publishers love relative frequency tables because they can present information without having to list any confusing (or maybe comprom