WEEK 2: SUMMARIZING AND GRAPHING DATA
Complete this guide
on your computer
as you read.
CHAPTER 2.1 and 2.2
In Chapter 2, 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 21
(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 22
.
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 4150 years old when they won?
12
How many actresses were 5160 years old when they won?
2
How many actresses were 4160 years old when they won?
14
Now look at Table 23.
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 compromising?) data sets.
By the same token, the reader has to
trust that the publisher has data sets that are accurate and representative of their claims.
What % of actresses were 4150 years old when they won?
16%
What % of actresses were 5160 years old when they won?
3%
What % of actresses were 4160 years old when they won?
19%
Now look at Table 28.
COMPARATIVE RELATIVE FREQUENCY tables are even
more fun to use.
We can fairly readily see that young actresses are more likely to win an
award but actors can expect that they will have to prove themselves for a longer period of
time before winning any award. Assuming the data is correct, then we might be able to
conclude that males and females are not necessarily judged by the same sets of rules by
the Oscar Awards Committees.
A comparative relative frequency table allows us to
critically evaluate the data. Any gap in the data larger than 5% is considered significant.
What % of actresses were 4150 years old when they won?
16%
What % of actors were 4150 years old when they won?
39%
What is the % discrepancy between the two groups?
15%
1
This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Now you can go ahead and read pages 5053 and then complete the matching question
below with respect to Table 22.
Lower class limit
21, 31, 41, 51, 61, 71
Upper class limit
30, 40, 50, 60, 70, 80
Class boundaries
30.5, 40.5, 50.5, 60.5. 70.5 (separates the classes)
Class midpoints
25.5, 35.5, 45.5, 55.5, 65.5, 75.5 (middle of each class)
Class width
(maximum#minimum#)/# classes = (8021)/ 6 = 10
If you were able to select the correct matches with no problem, great, if the concept is
still a little confusing, then try reading the definitions and examples below.
This is the end of the preview.
Sign up
to
access the rest of the document.
 Spring '09
 MoSeKim
 Frequency, Frequency distribution, Histogram, relative frequency

Click to edit the document details