1. 3 2 Chapter Organizing and Summarizing Data
© 2010 Pearson Prentice Hall. All rights reserved

2. Objectives Section 2.1 Organizing Qualitative Data
Organize Qualitative Data
Construct Bar Graphs
Construct Pie Charts
© 2010 Pearson Prentice Hall. All rights reserved

3. © 2010 Pearson Prentice Hall. All rights reserved
When data is collected from a survey or designed experiment, they must be organized into a manageable form. Data that is not organized is referred to as raw data.
Ways to Organize Data
Tables
Graphs
Numerical Summaries (Chapter 3)
2-3
© 2010 Pearson Prentice Hall. All rights reserved

4. © 2010 Pearson Prentice Hall. All rights reserved
Objective 1
Organize Qualitative Data in Tables
2-4
© 2010 Pearson Prentice Hall. All rights reserved

5. © 2010 Pearson Prentice Hall. All rights reserved
A frequency distribution lists each category of data and the number of occurrences for each category of data.
2-5
© 2010 Pearson Prentice Hall. All rights reserved

6. © 2010 Pearson Prentice Hall. All rights reserved
EXAMPLE Organizing Qualitative Data into a Frequency Distribution
The data on the next slide represent the color of M&Ms in a bag of plain M&Ms.
Construct a frequency distribution of the color of plain M&Ms.
2-6
© 2010 Pearson Prentice Hall. All rights reserved

7. © 2010 Pearson Prentice Hall. All rights reserved
The relative frequency is the proportion (or percent) of observations within a category and is found using the formula:
A relative frequency distribution lists the relative frequency of each category of data.
2-7
© 2010 Pearson Prentice Hall. All rights reserved

8. © 2010 Pearson Prentice Hall. All rights reserved
EXAMPLE Organizing Qualitative Data into a Relative Frequency Distribution
Use the frequency distribution obtained in the prior example to construct a relative frequency distribution of the color of plain M&Ms.
2-8
© 2010 Pearson Prentice Hall. All rights reserved

9. © 2010 Pearson Prentice Hall. All rights reserved
Frequency table
2-9
© 2010 Pearson Prentice Hall. All rights reserved

10. © 2010 Pearson Prentice Hall. All rights reserved
Relative Frequency
0.2222
0.2
0.1333
0.0667
0.1111
2-10
© 2010 Pearson Prentice Hall. All rights reserved

11. © 2010 Pearson Prentice Hall. All rights reserved
Objective 2
Construct Bar Graphs
2-11
© 2010 Pearson Prentice Hall. All rights reserved

12. © 2010 Pearson Prentice Hall. All rights reserved
A bar graph is constructed by labeling each category of data on either the horizontal or vertical axis and the frequency or relative frequency of the category on the other axis.
2-12
© 2010 Pearson Prentice Hall. All rights reserved

13. © 2010 Pearson Prentice Hall. All rights reserved
EXAMPLE Constructing a Frequency and Relative Frequency Bar Graph
Use the M&M data to construct
a frequency bar graph and
a relative frequency bar graph.
2-13
© 2010 Pearson Prentice Hall. All rights reserved

14. © 2010 Pearson Prentice Hall. All rights reserved
2-14
© 2010 Pearson Prentice Hall. All rights reserved

15. © 2010 Pearson Prentice Hall. All rights reserved
2-15
© 2010 Pearson Prentice Hall. All rights reserved

16. © 2010 Pearson Prentice Hall. All rights reserved
A Pareto chart is a bar graph where the bars are drawn in decreasing order of frequency or relative frequency.
2-16
© 2010 Pearson Prentice Hall. All rights reserved

17. © 2010 Pearson Prentice Hall. All rights reserved
Pareto Chart
2-17
© 2010 Pearson Prentice Hall. All rights reserved

18. © 2010 Pearson Prentice Hall. All rights reserved
EXAMPLE Comparing Two Data Sets
The following data represent the marital status (in millions) of U.S. residents 18 years of age or older in 1990 and Draw a side-by-side relative frequency bar graph of the data.
Marital Status
1990
2006
Never married
40.4
55.3
Married
112.6
127.7
Widowed
13.8
13.9
Divorced
15.1
22.8
2-18
© 2010 Pearson Prentice Hall. All rights reserved

19. © 2010 Pearson Prentice Hall. All rights reserved
Marital Status in 1990 vs. 2006
1990
Relative Frequency
2006
Marital Status
2-19
© 2010 Pearson Prentice Hall. All rights reserved

20. Objective 3 Construct Pie Charts 2-20
© 2010 Pearson Prentice Hall. All rights reserved
2-20

21. A pie chart is a circle divided into sectors
A pie chart is a circle divided into sectors. Each sector represents a category of data. The area of each sector is proportional to the frequency of the category.
© 2010 Pearson Prentice Hall. All rights reserved
2-21

22. EXAMPLE Constructing a Pie Chart
The following data represent the marital status (in millions) of U.S. residents 18 years of age or older in Draw a pie chart of the data.
Marital Status
Frequency
Never married
55.3
Married
127.7
Widowed
13.9
Divorced
22.8
2-22
© 2010 Pearson Prentice Hall. All rights reserved

23. Section 2.2 Organizing Quantitative Data: The Popular Displays
Objectives
Organize discrete data in tables
Construct histograms of discrete data
Organize continuous data in tables
Construct histograms of continuous data
Draw stem-and-leaf plots
Draw dot plots
Identify the shape of a distribution
2-23
© 2010 Pearson Prentice Hall. All rights reserved

24. The first step in summarizing quantitative data is to determine whether the data is discrete or continuous. If the data is discrete and there are relatively few different values of the variable, the categories of data will be the observations (as in qualitative data). If the data is discrete, but there are many different values of the variable, or if the data is continuous, the categories of data (called classes) must be created using intervals of numbers.
© 2010 Pearson Prentice Hall. All rights reserved
2-24

25. Objective 1 Organize discrete data in tables 2-25
© 2010 Pearson Prentice Hall. All rights reserved